graphical representation of covid 19 in india

India: Coronavirus Pandemic Country Profile

Research and data: Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Daniel Gavrilov, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina, and Max Roser

• Coronavirus
• Data explorer
• Hospitalizations

Vaccinations

• Mortality risk
• Excess mortality
• Policy responses

Build on top of our work freely

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• All our research and visualizations are free for everyone to use for all purposes

Select countries to show in all charts

Confirmed cases.

• What is the daily number of confirmed cases?
• Daily confirmed cases: how do they compare to other countries?
• What is the cumulative number of confirmed cases?
• Cumulative confirmed cases: how do they compare to other countries?
• Biweekly cases : where are confirmed cases increasing or falling?
• Global cases in comparison: how are cases changing across the world?

India: What is the daily number of confirmed cases?

Related charts:.

Which world regions have the most daily confirmed cases?

This chart shows the number of confirmed COVID-19 cases per day . This is shown as the seven-day rolling average.

What is important to note about these case figures?

• The reported case figures on a given date do not necessarily show the number of new cases on that day – this is due to delays in reporting.
• The number of confirmed cases is lower than the true number of infections – this is due to limited testing. In a separate post we discuss how models of COVID-19 help us estimate the true number of infections .

→ We provide more detail on these points in our page on Cases of COVID-19 .

Five quick reminders on how to interact with this chart

• By clicking on Edit countries and regions you can show and compare the data for any country in the world you are interested in.
• If you click on the title of the chart, the chart will open in a new tab. You can then copy-paste the URL and share it.
• You can switch the chart to a logarithmic axis by clicking on ‘LOG’.
• If you move both ends of the time-slider to a single point you will see a bar chart for that point in time.
• Map view: switch to a global map of confirmed cases using the ‘MAP’ tab at the bottom of the chart.

India: Daily confirmed cases: how do they compare to other countries?

Differences in the population size between different countries are often large. To compare countries, it is insightful to look at the number of confirmed cases per million people – this is what the chart shows.

Keep in mind that in countries that do very little testing the actual number of cases can be much higher than the number of confirmed cases shown here.

Three tips on how to interact with this map

• By clicking on any country on the map you see the change over time in this country.
• By moving the time slider (below the map) you can see how the global situation has changed over time.
• You can focus on a particular world region using the dropdown menu to the top-right of the map.

India: What is the cumulative number of confirmed cases?

Which world regions have the most cumulative confirmed cases?

How do the number of tests compare to the number of confirmed COVID-19 cases?

The previous charts looked at the number of confirmed cases per day – this chart shows the cumulative number of confirmed cases since the beginning of the COVID-19 pandemic.

In all our charts you can download the data

We want everyone to build on top of our work and therefore we always make all our data available for download. Click on the ‘Download’-tab at the bottom of the chart to download the shown data for all countries in a .csv file.

India: Cumulative confirmed cases: how do they compare to other countries?

This chart shows the cumulative number of confirmed cases per million people.

India: Biweekly cases : where are confirmed cases increasing or falling?

Why is it useful to look at biweekly changes in confirmed cases.

For all global data sources on the pandemic, daily data does not necessarily refer to the number of new confirmed cases on that day – but to the cases  reported  on that day.

Since reporting can vary significantly from day to day – irrespectively of any actual variation of cases – it is helpful to look at a longer time span that is less affected by the daily variation in reporting. This provides a clearer picture of where the pandemic is accelerating, staying the same, or reducing.

The first map here provides figures on the number of confirmed cases in the last two weeks. To enable comparisons across countries it is expressed per million people of the population.

And the second map shows the percentage change (growth rate) over this period: blue are all those countries in which the case count in the last two weeks was lower than in the two weeks before. In red countries the case count has increased.

What is the weekly number of confirmed cases?

What is the weekly change (growth rate) in confirmed cases?

India: Global cases in comparison: how are cases changing across the world?

In our page on COVID-19 cases , we provide charts and maps on how the number and change in cases compare across the world.

Confirmed deaths

• What is the daily number of confirmed deaths?
• Daily confirmed deaths: how do they compare to other countries?
• What is the cumulative number of confirmed deaths?
• Cumulative confirmed deaths: how do they compare to other countries?
• Biweekly deaths : where are confirmed deaths increasing or falling?
• Global deaths in comparison: how are deaths changing across the world?

India: What is the daily number of confirmed deaths?

Which world regions have the most daily confirmed deaths?

This chart shows t he number of confirmed COVID-19 deaths per day .

Three points on confirmed death figures to keep in mind

All three points are true for all currently available international data sources on COVID-19 deaths:

• The actual death toll from COVID-19 is likely to be higher than the number of confirmed deaths – this is due to limited testing and challenges in the attribution of the cause of death. The difference between confirmed deaths and actual deaths varies by country.
• How COVID-19 deaths are determined and recorded may differ between countries.
• The death figures on a given date do not necessarily show the number of new deaths on that day, but the deaths  reported  on that day. Since reporting can vary significantly from day to day – irrespectively of any actual variation of deaths – it is helpful to view the seven-day rolling average of the daily figures as we do in the chart here.

→ We provide more detail on these three points in our page on Deaths from COVID-19 .

India: Daily confirmed deaths: how do they compare to other countries?

This chart shows the daily confirmed deaths per million people of a country’s population.

Why adjust for the size of the population?

Differences in the population size between countries are often large, and the COVID-19 death count in more populous countries tends to be higher . Because of this it can be insightful to know how the number of confirmed deaths in a country compares to the number of people who live there, especially when comparing across countries.

For instance, if 1,000 people died in Iceland, out of a population of about 340,000, that would have a far bigger impact than the same number dying in the United States, with its population of 331 million. 1 This difference in impact is clear when comparing deaths per million people of each country’s population – in this example it would be roughly 3 deaths/million people in the US compared to a staggering 2,941 deaths/million people in Iceland.

India: What is the cumulative number of confirmed deaths?

Which world regions have the most cumulative confirmed deaths?

The previous charts looked at the number of confirmed deaths per day – this chart shows the cumulative number of confirmed deaths since the beginning of the COVID-19 pandemic.

India: Cumulative confirmed deaths: how do they compare to other countries?

This chart shows the cumulative number of confirmed deaths per million people.

India: Biweekly deaths : where are confirmed deaths increasing or falling?

Why is it useful to look at biweekly changes in deaths.

For all global data sources on the pandemic, daily data does not necessarily refer to deaths on that day – but to the deaths  reported  on that day.

Since reporting can vary significantly from day to day – irrespectively of any actual variation of deaths – it is helpful to look at a longer time span that is less affected by the daily variation in reporting. This provides a clearer picture of where the pandemic is accelerating, staying the same, or reducing.

The first map here provides figures on the number of confirmed deaths in the last two weeks. To enable comparisons across countries it is expressed per million people of the population.

And the second map shows the percentage change (growth rate) over this period: blue are all those countries in which the death count in the last two weeks was lower than in the two weeks before. In red countries the death count has increased.

What is the weekly number of confirmed deaths?

What is the weekly change (growth rate) in confirmed deaths?

India: Global deaths in comparison: how are deaths changing across the world?

In our page on COVID-19 deaths , we provide charts and maps on how the number and change in deaths compare across the world.

• How many COVID-19 vaccine doses are administered daily ?
• How many COVID-19 vaccine doses have been administered in total ?
• What share of the population has received  at least one dose  of the COVID-19 vaccine?
• What share of the population has  completed the initial vaccination protocol ?
• Global vaccinations in comparison: which countries are vaccinating most rapidly?

India: How many COVID-19 vaccine doses are administered daily ?

How many vaccine doses are administered each day (not population adjusted)?

This chart shows the daily number of COVID-19 vaccine doses administered per 100 people in a given population . This is shown as the rolling seven-day average. Note that this is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g., people receive multiple doses).

India: How many COVID-19 vaccine doses have been administered in total ?

How many vaccine doses have been administered in total (not population adjusted)?

This chart shows the total number of COVID-19 vaccine doses administered per 100 people within a given population. Note that this is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime as several available COVID vaccines require multiple doses.

India: What share of the population has received  at least one dose  of the COVID-19 vaccine?

How many people have received at least one vaccine dose?

This chart shows the share of the total population that has received at least one dose of the COVID-19 vaccine. This may not equal the share with a complete initial protocol if the vaccine requires two doses. If a person receives the first dose of a 2-dose vaccine, this metric goes up by 1. If they receive the second dose, the metric stays the same.

India: What share of the population has  completed the initial vaccination protocol ?

How many people have completed the initial vaccination protocol?

The following chart shows the share of the total population that has completed the initial vaccination protocol. If a person receives the first dose of a 2-dose vaccine, this metric stays the same. If they receive the second dose, the metric goes up by 1.

This data is only available for countries which report the breakdown of doses administered by first and second doses.

India: Global vaccinations in comparison: which countries are vaccinating most rapidly?

In our page on COVID-19 vaccinations, we provide maps and charts on how the number of people vaccinated compares across the world.

Testing for COVID-19

• The positive rate
• The scale of testing compared to the scale of the outbreak
• How many tests are performed each day ?
• Global testing in comparison: how is testing changing across the world?

India: The positive rate

Here we show the share of reported tests returning a positive result – known as the positive rate.

The positive rate can be a good metric for how adequately countries are testing because it can indicate the level of testing relative to the size of the outbreak. To be able to properly monitor and control the spread of the virus, countries with more widespread outbreaks need to do more testing.

It can also be helpful to think of the positive rate the other way around:

How many tests have countries done for each confirmed case in total across the outbreak?

India: The scale of testing compared to the scale of the outbreak

How do daily tests and daily new confirmed cases compare when not adjusted for population ?

This scatter chart provides another way of seeing the extent of testing relative to the scale of the outbreak in different countries.

The chart shows the daily number of tests (vertical axis) against the daily number of new confirmed cases (horizontal axis), both per million people.

India: How many tests are performed each day ?

This chart shows the number of  daily  tests per thousand people. Because the number of tests is often volatile from day to day, we show the figures as a seven-day rolling average.

What is counted as a test?

The number of tests does not refer to the same thing in each country – one difference is that some countries report the number of people tested, while others report the number of tests (which can be higher if the same person is tested more than once). And other countries report their testing data in a way that leaves it unclear what the test count refers to exactly.

We indicate the differences in the chart and explain them in detail in our accompanying  source descriptions .

India: Global testing in comparison: how is testing changing across the world?

In our page on COVID-19 testing , we provide charts and maps on how the number and change in tests compare across the world.

Case fatality rate

• What does the data on deaths and cases tell us about the mortality risk of COVID-19?
• The case fatality rate
• Learn in more detail about the mortality risk of COVID-19

India: What does the data on deaths and cases tell us about the mortality risk of COVID-19?

To understand the risks and respond appropriately we would also want to know the mortality risk of COVID-19 – the likelihood that someone who is infected with the disease will die from it.

We look into this question in more detail on our page about the mortality risk of COVID-19 , where we explain that this requires us to know – or estimate – the number of total cases and the final number of deaths for a given infected population.

Because these are not known , we discuss what the current data on confirmed deaths and cases can and can not tell us about the risk of death. This chart shows both those metrics.

India: The case fatality rate

Related chart:.

How do the cumulative number of confirmed deaths and cases compare?

The case fatality rate is simply the ratio of the two metrics shown in the chart above.

The case fatality rate is the number of confirmed deaths divided by the number of confirmed cases.

This chart here plots the CFR calculated in just that way. 

During an outbreak – and especially when the total number of cases is not known – one has to be very careful in interpreting the CFR . We wrote a  detailed explainer  on what can and can not be said based on current CFR figures.

India: Learn in more detail about the mortality risk of COVID-19

Learn what we know about the mortality risk of COVID-19 and explore the data used to calculate it.

Government Responses

• Government Stringency Index

To understand how governments have responded to the pandemic, we rely on data from the Oxford Coronavirus Government Response Tracker  (OxCGRT), which is published and managed by researchers at the Blavatnik School of Government at the University of Oxford.

This tracker collects publicly available information on 17 indicators of government responses, spanning containment and closure policies (such as school closures and restrictions in movement); economic policies; and health system policies (such as testing regimes).

How have countries responded to the pandemic?

Travel bans, stay-at-home restrictions, school closures – how have countries responded to the pandemic? Explore the data on all policy measures.

India: Government Stringency Index

The chart here shows how governmental response has changed over time. It shows the Government Stringency Index – a composite measure of the strictness of policy responses.

The index on any given day is calculated as the mean score of nine policy measures, each taking a value between 0 and 100. See the authors’  full description  of how this index is calculated.

A higher score indicates a stricter government response (i.e. 100 = strictest response).

The OxCGRT project calculates this index using nine specific measures, including:

• school and workplace closures;
• restrictions on public gatherings;
• transport restrictions;
• and stay-at-home requirements.

You can see all of these separately on our page on policy responses . There you can also compare these responses in countries across the world.

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Computer Science > Computers and Society

Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field.

Title: Exploring and Visualizing COVID-19 Trends in India: Vulnerabilities and Mitigation Strategies

Abstract: Visualizing data plays a pivotal role in portraying important scientific information. Hence, visualization techniques aid in displaying relevant graphical interpretations from the varied structures of data, which is found otherwise. In this paper, we explore the COVID-19 pandemic influence trends in the subcontinent of India in the context of how far the infection rate spiked in the year 2020 and how the public health division of the country India has helped to curb the spread of the novel virus by installing vaccination centers across the diaspora of the country. The paper contributes to the empirical study of understanding the impact caused by the novel virus to the country by doing extensive explanatory data analysis of the data collected from the official government portal. Our work contributes to the understanding that data visualization is prime in understanding public health problems and beyond and taking necessary measures to curb the existing pandemic.

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India Coronavirus Map and Case Count

The New York Times Updated March 10, 2023

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Tracking Coronavirus in India: Latest Map and Case Count

New reported cases.

Vaccinations

Fully vaccinated.

See more details ›

Latest trends

• An average of 336 cases per day were reported in India in the last week. Cases have increased by 145 percent from the average two weeks ago. Deaths remained at about the same level .
• Since the beginning of the pandemic, a total of 44,690,738 cases have been reported . At least 1 in 2,574 residents have died from the coronavirus, a total of 530,779 deaths .
• May 2021 was the month with the highest average cases and deaths in India.
• The official Covid-19 figures in India grossly understate the true scale of the pandemic in the country. Here’s what to know about India’s coronavirus crisis .

Latest trends by state and union territory

This table is sorted by places with the most cases per 100,000 residents in the last seven days. Charts show change in daily averages and are each on their own scale.

How trends have changed in India

About the data.

Data for India comes from the Center for Systems Science and Engineering at Johns Hopkins University . Population data from ESRI .

The Times has identified reporting anomalies or methodology changes in the data.

• June 16, 2020: India announced a backlog of deaths.

Confirmed cases and deaths , which are widely considered to be an undercount of the true toll, are counts of individuals whose coronavirus infections were confirmed by a molecular laboratory test. Probable cases and deaths count individuals who meet criteria for other types of testing, symptoms and exposure, as developed by national and local governments.

Governments often revise data or report a single-day large increase in cases or deaths from unspecified days without historical revisions, which can cause an irregular pattern in the daily reported figures. The Times is excluding these anomalies from seven-day averages when possible. For agencies that do not report data every day, variation in the schedule on which cases or deaths are reported, such as around holidays, can also cause an irregular pattern in averages. The Times uses an adjustment method to vary the number of days included in an average to remove these irregularities.

Tracking the Coronavirus

By Jordan Allen, Sarah Almukhtar , Aliza Aufrichtig , Anne Barnard, Matthew Bloch , Penn Bullock, Sarah Cahalan, Weiyi Cai, Julia Calderone, Keith Collins , Matthew Conlen, Lindsey Cook, Gabriel Gianordoli , Amy Harmon , Rich Harris , Adeel Hassan , Jon Huang , Danya Issawi, Danielle Ivory , K.K. Rebecca Lai , Alex Lemonides, Eleanor Lutz , Allison McCann , Richard A. Oppel Jr. , Jugal K. Patel , Alison Saldanha, Kirk Semple, Shelly Seroussi, Julie Walton Shaver, Amy Schoenfeld Walker , Anjali Singhvi , Charlie Smart , Mitch Smith , Albert Sun , Rumsey Taylor , Lisa Waananen Jones, Derek Watkins , Timothy Williams , Jin Wu and Karen Yourish .   ·   Reporting was contributed by Jeff Arnold, Ian Austen , Mike Baker , Brillian Bao, Ellen Barry , Shashank Bengali , Samone Blair, Nicholas Bogel-Burroughs, Aurelien Breeden, Elisha Brown, Emma Bubola, Maddie Burakoff, Alyssa Burr, Christopher Calabrese, Julia Carmel, Zak Cassel, Robert Chiarito, Izzy Colón, Matt Craig, Yves De Jesus, Brendon Derr, Brandon Dupré, Melissa Eddy, John Eligon, Timmy Facciola, Bianca Fortis, Jake Frankenfield, Matt Furber, Robert Gebeloff, Thomas Gibbons-Neff, Matthew Goldstein , Grace Gorenflo, Rebecca Griesbach, Benjamin Guggenheim, Barbara Harvey, Lauryn Higgins, Josh Holder, Jake Holland, Anna Joyce, John Keefe , Ann Hinga Klein, Jacob LaGesse, Alex Lim, Alex Matthews, Patricia Mazzei, Jesse McKinley, Miles McKinley, K.B. Mensah, Sarah Mervosh, Jacob Meschke, Lauren Messman, Andrea Michelson, Jaylynn Moffat-Mowatt, Steven Moity, Paul Moon, Derek M. Norman, Anahad O’Connor, Ashlyn O’Hara, Azi Paybarah, Elian Peltier, Richard Pérez-Peña , Sean Plambeck, Laney Pope, Elisabetta Povoledo, Cierra S. Queen, Savannah Redl, Scott Reinhard , Chloe Reynolds, Thomas Rivas, Frances Robles, Natasha Rodriguez, Jess Ruderman, Kai Schultz , Alex Schwartz, Emily Schwing, Libby Seline, Rachel Sherman, Sarena Snider, Brandon Thorp, Alex Traub, Maura Turcotte, Tracey Tully, Jeremy White , Kristine White, Bonnie G. Wong, Tiffany Wong, Sameer Yasir and John Yoon.   ·   Data acquisition and additional work contributed by Will Houp, Andrew Chavez, Michael Strickland, Tiff Fehr, Miles Watkins, Josh Williams , Nina Pavlich, Carmen Cincotti, Ben Smithgall, Andrew Fischer, Rachel Shorey , Blacki Migliozzi , Alastair Coote, Jaymin Patel, John-Michael Murphy, Isaac White, Steven Speicher, Hugh Mandeville, Robin Berjon, Thu Trinh, Carolyn Price, James G. Robinson, Phil Wells, Yanxing Yang, Michael Beswetherick, Michael Robles, Nikhil Baradwaj, Ariana Giorgi, Bella Virgilio, Dylan Momplaisir, Avery Dews, Bea Malsky, Ilana Marcus, Sean Cataguni and Jason Kao .

• Emergencies  /
• Coronavirus Disease (COVID-19) /
• India Situation Report

India Situation Reports

The WHO India Weekly COVID-19 Situational Report provides a comprehensive summary of the COVID-19 situation in India. The report provides an epidemiological overview of India, highlights WHO India operational updates on risk communication and community engagement, infection prevention and control, clinical management, operation support and logistics. The WHO Situational Report summarizes the severity of public health and social measures implemented in India and provides an update on pandemic vaccine deployment in the country.

India Situation Report 116 Coronavirus Disease (COVID-19)  22 July 2022

India Situation Report 115 Coronavirus Disease (COVID-19)  15 June 2022

India Situation Report 114 Coronavirus Disease (COVID-19)  18 May 2022

India Situation Report 113 Coronavirus Disease (COVID-19)  04 May 2022

India Situation Report 112 Coronavirus Disease (COVID-19)  20 April 2022

India Situation Report 111 Coronavirus Disease (COVID-19)  06 April 2022

India Situation Report 110 Coronavirus Disease (COVID-19)  23 March 2022

India Situation Report 109 Coronavirus Disease (COVID-19)  09 March 2022

India Situation Report 108 Coronavirus Disease (COVID-19)  23 February 2022

India Situation Report 107 Coronavirus Disease (COVID-19)  16 February 2022

India Situation Report 106 Coronavirus Disease (COVID-19)  09 February 2022

India Situation Report 105 Coronavirus Disease (COVID-19)  02 February 2022

India Situation Report 104 Coronavirus Disease (COVID-19)  26 January 2022

India Situation Report 103 Coronavirus Disease (COVID-19)  19 January 2022

India Situation Report 102 Coronavirus Disease (COVID-19)  12 January 2022

India Situation Report 101 Coronavirus Disease (COVID-19)  05 January 2022

India Situation Report 100 Coronavirus Disease (COVID-19)  29 December 2021

India Situation Report 99 Coronavirus Disease (COVID-19)  22 December 2021

India Situation Report 98 Coronavirus Disease (COVID-19)  15 December 2021

India Situation Report 97 Coronavirus Disease (COVID-19)  8 December 2021

India Situation Report 96 Coronavirus Disease (COVID-19)  1 December 2021

India Situation Report 95 Coronavirus Disease (COVID-19)  24 November 2021

India Situation Report 94 Coronavirus Disease (COVID-19)  17 November 2021

India Situation Report 93 Coronavirus Disease (COVID-19)  10 November 2021

India Situation Report 92 Coronavirus Disease (COVID-19)  03 November 2021

India Situation Report 91 Coronavirus Disease (COVID-19)  27 October 2021

India Situation Report 90 Coronavirus Disease (COVID-19)  20 October 2021

India Situation Report 89 Coronavirus Disease (COVID-19)  13 October 2021

India Situation Report 88 Coronavirus Disease (COVID-19)  6 October 2021

India Situation Report 87 Coronavirus Disease (COVID-19)  29 September 2021

India Situation Report 86 Coronavirus Disease (COVID-19)  22 September 2021

India Situation Report 85 Coronavirus Disease (COVID-19)  15 September 2021

India Situation Report 84 Coronavirus Disease (COVID-19)  8 September 2021

India Situation Report 83 Coronavirus Disease (COVID-19)  1 September 2021

India Situation Report 82 Coronavirus Disease (COVID-19)  25 August 2021

India Situation Report 81 Coronavirus Disease (COVID-19)  18 August 2021

India Situation Report 80 Coronavirus Disease (COVID-19)  11 August 2021

India Situation Report 79 Coronavirus Disease (COVID-19)  4 August 2021

India Situation Report 78 Coronavirus Disease (COVID-19)  28 July 2021

India Situation Report 77 Coronavirus Disease (COVID-19)  21 July 2021

India Situation Report 76 Coronavirus Disease (COVID-19)  14 July 2021

India Situation Report 75 Coronavirus Disease (COVID-19)  7 July 2021

India Situation Report 74 Coronavirus Disease (COVID-19)  30 June 2021

India Situation Report 73 Coronavirus Disease (COVID-19)  23 June 2021

India Situation Report 72 Coronavirus Disease (COVID-19)  16 June 2021

India Situation Report 71 Coronavirus Disease (COVID-19)  9 June 2021

India Situation Report 70 Coronavirus Disease (COVID-19)  2 June 2021

India Situation Report 69 Coronavirus Disease (COVID-19)  26 May 2021

India Situation Report 68 Coronavirus Disease (COVID-19)  19 May 2021

India Situation Report 67 Coronavirus Disease (COVID-19)  12 May 2021

India Situation Report 66 Coronavirus Disease (COVID-19)  5 May 2021

India Situation Report 65 Coronavirus Disease (COVID-19)  28 April 2021

India Situation Report 64 Coronavirus Disease (COVID-19)  21 April 2021

India Situation Report 63 Coronavirus Disease (COVID-19)  12 April 2021

India Situation Report 62 Coronavirus Disease (COVID-19)  5 April 2021

India Situation Report 61 Coronavirus Disease (COVID-19)  29 March 2021

India Situation Report 60 Coronavirus Disease (COVID-19)  22 March 2021

India Situation Report 59 Coronavirus Disease (COVID-19)  15 March 2021

India Situation Report 58 Coronavirus Disease (COVID-19)  10 March 2021

India Situation Report 57 Coronavirus Disease (COVID-19)  1 March 2021

India Situation Report 56 Coronavirus Disease (COVID-19)  22 February 2021

India Situation Report-55 Coronavirus Disease (COVID-19)  15 February 2021

India Situation Report-54 Coronavirus Disease (COVID-19)  8 February 2021

India Situation Report-53 Coronavirus Disease (COVID-19)  1 February 2021

India Situation Report-52 Coronavirus Disease (COVID-19)  25 January 2021

India Situation Report-51 Coronavirus Disease (COVID-19)  18 January 2021

India Situation Report-50 Coronavirus Disease (COVID-19)  11 January 2021

India Situation Report-49 Coronavirus Disease (COVID-19)  4 January 2021

India Situation Report-48 Coronavirus Disease (COVID-19)  28 December 2020

India Situation Report-47 Coronavirus Disease (COVID-19)  21 December 2020

India Situation Report-46 Coronavirus Disease (COVID-19)  14 December 2020

India Situation Report-45 Coronavirus Disease (COVID-19)  7 December 2020

India Situation Report-44 Coronavirus Disease (COVID-19)  30 November 2020

India Situation Report-43 Coronavirus Disease (COVID-19)  23 November 2020

India Situation Report-42 Coronavirus Disease (COVID-19)  16 November 2020

India Situation Report-41 Coronavirus Disease (COVID-19)  9 November 2020

India Situation Report - 40 Coronavirus Disease (COVID-19)  2 November 2020

India Situation Report - 39 Coronavirus Disease (COVID-19)  26 October 2020

India Situation Report - 38 Coronavirus Disease (COVID-19)  19 October 2020

India Situation Report - 37 Coronavirus Disease (COVID-19)  12 October 2020

India Situation Report - 36 Coronavirus Disease (COVID-19)  5 October 2020

India Situation Report - 35 Coronavirus Disease (COVID-19)  28 September 2020

India Situation Report - 34 Coronavirus Disease (COVID-19)  21 September 2020

India Situation Report - 33 Coronavirus Disease (COVID-19)  14 September 2020

India Situation Report - 32 Coronavirus Disease (COVID-19)  7 September 2020

India Situation Report - 31 Coronavirus Disease (COVID-19)  1 September 2020

India Situation Report - 30 Coronavirus Disease (COVID-19)  24 August 2020

India Situation Report - 29 Coronavirus Disease (COVID-19)  17 August 2020

India Situation Report - 28 Coronavirus Disease (COVID-19)  10 August 2020

India Situation Report - 27 Coronavirus Disease (COVID-19)  2 August 2020

India Situation Report - 26 Coronavirus Disease (COVID-19)  26 July 2020

India Situation Report - 25 Coronavirus Disease (COVID-19)  19 July 2020

India Situation Report - 24 Coronavirus Disease (COVID-19)  12 July 2020

India Situation Report - 23 Coronavirus Disease (COVID-19)  5 July 2020

India Situation Report - 22 Coronavirus Disease (COVID-19)  28 June 2020

India Situation Report - 21 Coronavirus Disease (COVID-19)  21 June 2020

India Situation Report - 20 Coronavirus Disease (COVID-19)  14 June 2020

India Situation Report - 19 Coronavirus Disease (COVID-19)  7 June 2020

India Situation Report - 18 Coronavirus Disease (COVID-19)  31 May 2020

India Situation Report - 17 Coronavirus Disease (COVID-19)  24 May 2020

India Situation Report - 16 Coronavirus Disease (COVID-19)  17 May 2020

India Situation Report - 15 Coronavirus Disease (COVID-19)  10 May 2020

India Situation Report - 14 Coronavirus Disease (COVID-19)  3 May 2020

India Situation Report - 13 Coronavirus Disease (COVID-19)  26 April 2020

India Situation Report - 12 Coronavirus Disease (COVID-19)  19 April 2020

India Situation Report - 11 Coronavirus Disease (COVID-19)  12 April 2020

India Situation Report - 10 Coronavirus Disease (COVID-19)  5 April 2020

India Situation Report - 9 Coronavirus Disease (COVID-19)  28 March 2020

India Situation Report - 8 Coronavirus Disease (COVID-19)  22 March 2020

India Situation Report - 7 Coronavirus Disease (COVID-19)  14 March 2020

India Situation Report - 6 Coronavirus Disease (COVID-19)  9 March 2020

India Situation Report - 5 Coronavirus Disease (COVID-19)  28 February 2020

India Situation Report - 4 Coronavirus Disease (COVID-19)  21 February 2020

India Situation Report - 2 Novel Coronavirus (2019-nCoV)  6 February 2020

India Situation Report - 1 Novel Coronavirus (2019-nCoV)  31 January 2020

Last updated July 15, 2022

There have been 43,710,027 infections and 525,604 coronavirus-related deaths reported in the country since the pandemic began.

Daily reported trends

Trends by state and union territory, you need to know.

COVID-19 infections and deaths numbers at this level are sensitive to many reporting issues that may cause underreporting such as the availability of wide-spread testing and differences in the health care infrastructure between regions.

SOURCE: Data from covid19india.org

How India compares

There is no one perfect statistic to compare the outbreaks different countries have experienced during this pandemic. Looking at a variety of metrics gives you a more complete view of the virus’ toll on each country.

These charts show several different statistics, each with their own strengths and weaknesses, that mark the various ways each country’s outbreak compares in its region and the world.

What it tells you...

Gives the true human toll of the virus on a country.

What it doesn’t

Can minimize the scale of the virus’ impact on smaller countries.

Infections in Asia and the Middle East

Infections, globally, deaths in asia and the middle east, deaths, globally, about this data.

Reuters is collecting daily COVID-19 infections and deaths data for 240 countries and territories around the world, updated regularly throughout each day.

Every country reports those figures a little differently and, inevitably, misses undiagnosed infections and deaths. With this project we are focusing on the trends within countries as they try to contain the virus’ spread, whether they are approaching or past peak infection rates, or if they are seeing a resurgence of infections or deaths.

Read more about our methodology

Where India COVID-19 data comes from

• Ministry of Health and Family Welfare, India

The latest coronavirus news from Reuters

Breaking international news & views, where u.s. coronavirus cases are on the rise.

The states where the outbreak is growing fastest

New normal: How far is safe enough?

How countries are adapting social distancing rules and what we know about the risks of coronavirus in public places.

Global tracker

• Liechtenstein
• North Macedonia
• Switzerland
• Isle of Man
• Bosnia and Herzegovina
• Netherlands
• Czech Republic
• Aland Islands
• Faroe Islands
• United Kingdom
• Vatican City

Asia and the Middle East

• United Arab Emirates
• Saudi Arabia
• South Korea
• Philippines
• Afghanistan
• Mainland China
• Timor-Leste
• Palestinian territories

Latin America and the Caribbean

• El Salvador
• Dominican Republic
• Cayman Islands
• Trinidad and Tobago
• French Guiana
• Saint Kitts and Nevis
• Saint Barthélemy
• Saint Lucia
• Bonaire, Sint Eustatius and Saba
• Saint Vincent and the Grenadines
• Saint Martin
• Antigua and Barbuda
• Falkland Islands
• Turks and Caicos
• Sint Maarten
• British Virgin Islands
• Equatorial Guinea
• Democratic Republic of the Congo
• Ivory Coast
• Burkina Faso
• Guinea-Bissau
• Sierra Leone
• Central African Republic
• Sao Tome and Principe
• South Africa
• Republic of the Congo
• Western Sahara
• South Sudan
• New Zealand
• New Caledonia
• French Polynesia
• Papua New Guinea

Northern America

• United States
• Saint Pierre and Miquelon

Data sources Local state agencies, local media, Oxford Coronavirus Government Response Tracker , Our World in Data , The World Bank , Reuters research

Design and development Gurman Bhatia , Prasanta Kumar Dutta , Chris Canipe and Jon McClure

Data collection and research Abhishek Manikandan, Aditya Munjuluru, Ahmed Farhatha, Amal Maqbool, Aniruddha Chakrabarty, Anna Banacka, Anna Pruchnicka, Anurag Maan, Anuron Kumar Mitra, Arpit Nayak, Arundhati Sarkar, Cate Cadell, Chaithra J, Chinmay Rautmare, Christine Chan, Daniela Desantis, Diana Mandia Alvarez, Elizaveta Gladun, Emily Isaacman, Enrico Sciacovelli, Gautami Khandke, Gayle Issa, Hardik Vyas, Harshith Aranya, Javier Lopez, Joao Manuel Vicente Mauricio, Juliette Portala, K. Sathya Narayanan, Kanupriya Kapoor, Kavya B., Lakshmi Siddappa, Lisa Shumaker, Mrinalika Roy, Nallur Sethuraman, Natalie Vaughan, Nikhil Subba, Olga Beskrovnova, Padraic Cassidy, Rohith Nair, Roshan Abraham, Sabahatjahan Contractor, Sanjana Vijay Kumar, Seerat Gupta, Shaina Ahluwalia, Shashank Nayar, Shreyasee Raj, Nivedha S., Simon Jennings, Sridhar Shrivathsa, Veronica Snoj, Wen Foo, Yajush Gupta, Aparupa Mazumder, Rittik Biswas and Maneesh Kumar

Translation Samuel Granados, Marco Hernandez, Erica Soh, Junko Tagashira, Momoko Honda, Kyoko Yamaguchi, Hiroko Terui, Pedro Fonseca, Olivier Cherfan, Kate Entringer, Dagmarah Mackos, Diana Mandia, Federica Mileo, Juliette Portala, Kate Entringer and Piotr Lipinski

• Health, Pharma & Medtech ›
• State of Health

Coronavirus (COVID-19) in India - statistics & facts

India witnessed an outbreak of the coronavirus , otherwise known as COVID-19, or SARS-CoV-2 in late January 2020 when three Indian students travelled to the southern state of Kerala from Wuhan in China - the epicenter of the outbreak. All three tested positive for COVID-19, confirming a local contagion. At the same time, several other cases were detected in other parts of the country, most of which were linked to people with a travel history to affected countries. Infections increased rapidly since March 2020 , with a significant growth in testing . The state of Kerala was commended for acting speedily in containing further spread of the virus. Thousands were consistently being placed in home or institutional quarantine , monitored for symptoms and infections. However, India had one of the lowest testing rates for the virus compared to other countries, despite ramping up over recent months. India’s healthcare workers and public officials were vigilant in their fight against the virus. Despite that, the country’s healthcare infrastructure may not be enough in the face of an epidemic. Data from 2017 showed India had less than 0.5 hospital beds per 1,000 people Moreover, the country’s population density was one of the highest in the world, making it harder to contain local transmissions if strict precaution measures are not followed. A lacking healthcare infrastructure also remains a major cause for concern. This was because of an inadequate public system leading to shifting the weight to the private sector, which was not prepared for an emergency of this magnitude. The Modi-led government announced a nation-wide lockdown in the end of March until May 2020. An increase in daily cases and a rising death count put India ahead with the fastest infection rate worldwide in August 2020. The south-western state of Maharashtra reported the most cases across the country. With restrictions having eased over the latter half of 2020, the economy was able to finally bounce back to a certain extent, especially during the festive season between September and October. However, with the start of 2021 complacency on issues such as social distancing and wearing masks led to a gradual increase in the number of infections. While the second wave was driven by the delta variant killing almost two million people, the omicron outbreak passed more gently with a significantly lower number of deaths. Eight weeks after the second wave of the pandemic began, India reported record numbers for daily infections and deaths for nearly two weeks in April 2021. Following the sudden surge in cases, several states and major cities went into some form of lockdown or curfew. With a scrambling demand for oxygen cylinders, over-capacitated hospitals and burned-out medical staff, the country’s health infrastructure was in distress, barely able to bring the situation under control. Aid was received from several countries including the United Kingdom, the United States, and Germany demonstrating solidarity in the fight against the virus. India’s civil struggle between the center and states for a more logically coordinated response and improved policies including the vaccine roll out program had been pressing during this time. This text provides general information. Statista assumes no liability for the information given being complete or correct. Due to varying update cycles, statistics can display more up-to-date data than referenced in the text. Show more - Description Published by Statista Research Department , Dec 19, 2023

Key insights

Detailed statistics

COVID-19 cases in India as of October 2023, by type

COVID-19 cases in Indian states 2023, by type

Editor’s Picks Current statistics on this topic

Health Economics

Health expenditure as a percentage of GDP in select countries 2023

Medical Technology

Cumulative vaccine doses administered for COVID-19 India 2021-2023

Further recommended statistics

Global overview.

• Basic Statistic COVID-19 cases worldwide as of May 2, 2023, by country or territory
• Basic Statistic Coronavirus (COVID-19) cases, recoveries, and deaths worldwide as of May 2, 2023
• Basic Statistic Number of tests for COVID-19 in most impacted countries worldwide as of Dec. 2022
• Basic Statistic Rate of COVID-19 testing in most impacted countries worldwide as of Dec. 22, 2022

COVID-19 cases worldwide as of May 2, 2023, by country or territory

Number of coronavirus (COVID-19) cases worldwide as of May 2, 2023, by country or territory

Coronavirus (COVID-19) cases, recoveries, and deaths worldwide as of May 2, 2023

Number of coronavirus (COVID-19) cases, recoveries, and deaths worldwide as of May 2, 2023

Number of tests for COVID-19 in most impacted countries worldwide as of Dec. 2022

Number of coronavirus (COVID-19) tests performed in the most impacted countries worldwide as of December 22, 2022*

Rate of COVID-19 testing in most impacted countries worldwide as of Dec. 22, 2022

Rate of coronavirus (COVID-19) tests performed in the most impacted countries worldwide as of December 22, 2022 (per million population)*

Numbers in India

• Basic Statistic COVID-19 cases in India as of October 2023, by type
• Basic Statistic COVID-19 confirmed, recovered and deceased cumulative cases in India 2020-2023
• Basic Statistic COVID-19 cases in Indian states 2023, by type
• Basic Statistic Cumulative COVID-19 tests India 2020-2023
• Basic Statistic Number of COVID-19 tests in India 2021, by state

Number of the coronavirus (COVID-19) cases across India as of October 20, 2023, by type (in 1,000s)

COVID-19 confirmed, recovered and deceased cumulative cases in India 2020-2023

Cumulative of the coronavirus (COVID-19) confirmed, recovered and deceased numbers across India from January 2020 to October 2023

Number of the coronavirus (COVID-19) cases across Indian states and union territories as of October 2023, by type

Cumulative COVID-19 tests India 2020-2023

Cumulative number of samples tested for the coronavirus (COVID-19) across India from April 2020 to October 2023 (in 1,000s)

Number of COVID-19 tests in India 2021, by state

Coronavirus (COVID-19) test numbers across India as of October 17, 2021, by state (in 1,000s)

State figures

• Basic Statistic COVID-19 cases in Maharashtra, India October 2023, by type
• Basic Statistic COVID-19 cases in Delhi, India October 2023, by type
• Basic Statistic COVID-19 cases in Gujarat, India October 2023, by type
• Basic Statistic COVID-19 cases in Tamil Nadu, India October 2023, by type

COVID-19 cases in Maharashtra, India October 2023, by type

Number of coronavirus (COVID-19) cases across Maharashtra in India as of October 20, 2023, by type

COVID-19 cases in Delhi, India October 2023, by type

Number of coronavirus (COVID-19) cases across Delhi, India as of October 20, 2023, by type

COVID-19 cases in Gujarat, India October 2023, by type

Number of coronavirus (COVID-19) cases across Gujarat, India as of October 20, 2023, by type

COVID-19 cases in Tamil Nadu, India October 2023, by type

Number of coronavirus (COVID-19) cases across Tamil Nadu, India as of October 20, 2023, by type

Second wave

• Premium Statistic COVID-19 impact on securing ICU beds in hospitals India 2021
• Premium Statistic COVID-19 impact on securing COVID management drugs India 2021
• Basic Statistic Cumulative vaccine doses administered for COVID-19 India 2021-2023
• Basic Statistic COVID-19 vaccine doses administered in India November 2023

COVID-19 impact on securing ICU beds in hospitals India 2021

Impact of coronavirus (COVID-19) on securing ICU beds in hospitals across India as of April 2021

COVID-19 impact on securing COVID management drugs India 2021

Impact of coronavirus (COVID-19) on securing COVID management drugs in India as of April 2021

Cumulative number of vaccine doses administered for the coronavirus (COVID-19) across India from January 2021 to October 2023 (in 1,000s)

COVID-19 vaccine doses administered in India November 2023

Cumulative number of COVID-19 vaccine doses administered across states and union territories in India as of November 13, 2023 (in 1,000s)

Public opinion

• Premium Statistic Share of people who felt stress during COVID-19 pandemic India 2020, by intensity
• Basic Statistic Fears and concerns with regard to COVID-19 in India 2020
• Basic Statistic COVID-19 lockdown activities in India 2020
• Basic Statistic Level of fear of contracting COVID-19 India 2020 by age group
• Basic Statistic Sentiment about impact of COVID-19 outbreak on schools remaining closed India 2020
• Basic Statistic Opinion on travel restrictions by government due to COVID-19 India 2020
• Basic Statistic Opinion on the coronavirus COVID-19 as a health issue India 2020

Share of people who felt stress during COVID-19 pandemic India 2020, by intensity

Share of people who felt stress during COVID-19 pandemic in India in 2020, by intensity

Fears and concerns with regard to COVID-19 in India 2020

Fears and concerns arising from the coronavirus (COVID-19) across India in April 2020

COVID-19 lockdown activities in India 2020

Activities involved in due to coronavirus (COVID-19) lockdown across India in April 2020

Level of fear of contracting COVID-19 India 2020 by age group

Level of fear of contracting the coronavirus (COVID-19) among Indians in 2020, by age group

Sentiment about impact of COVID-19 outbreak on schools remaining closed India 2020

Opinion on schools remaining closed through April and May due to the coronavirus (COVID-19) outbreak across India in March 2020

Opinion on travel restrictions by government due to COVID-19 India 2020

Opinion on government-implemented travel restrictions on foreign travelers due to the coronavirus (COVID-19) in India in March 2020

Opinion on the coronavirus COVID-19 as a health issue India 2020

Opinion on the coronavirus COVID-19 as a health issue across India in February 2020

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Open Access

Peer-reviewed

Research Article

Assessment of COVID-19 data reporting in 100+ websites and apps in India

Contributed equally to this work with: Varun Vasudevan, Abeynaya Gnanasekaran

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Institute for Computational & Mathematical Engineering, Stanford University, Stanford, California, United States of America

Roles Data curation, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliation All India Institute of Medical Sciences, New Delhi, India

Roles Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliation Public Policy and Health Systems Specialist, New Delhi, India

Affiliation Center for Disease Dynamics, Economics & Policy, New Delhi, India

Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Validation, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, United States of America

• Varun Vasudevan, 
• Abeynaya Gnanasekaran, 
• Bhavik Bansal, 
• Chandrakant Lahariya, 
• Giridara Gopal Parameswaran, 

• Published: April 15, 2022
• https://doi.org/10.1371/journal.pgph.0000329
• Peer Review
• Reader Comments

India is among the top three countries in the world both in COVID-19 case and death counts. With the pandemic far from over, timely, transparent, and accessible reporting of COVID-19 data continues to be critical for India’s pandemic efforts. We systematically analyze the quality of reporting of COVID-19 data in over one hundred government platforms (web and mobile) from India. Our analyses reveal a lack of granular data in the reporting of COVID-19 surveillance, vaccination, and vacant bed availability. As of 5 June 2021, age and gender distribution are available for less than 22% of cases and deaths, and comorbidity distribution is available for less than 30% of deaths. Amid rising concerns of undercounting cases and deaths in India, our results highlight a patchy reporting of granular data even among the reported cases and deaths. Furthermore, total vaccination stratified by healthcare workers, frontline workers, and age brackets is reported by only 14 out of India’s 36 subnationals (states and union territories). There is no reporting of adverse events following immunization by vaccine and event type. By showing what, where, and how much data is missing, we highlight the need for a more responsible and transparent reporting of granular COVID-19 data in India.

Citation: Vasudevan V, Gnanasekaran A, Bansal B, Lahariya C, Parameswaran GG, Zou J (2022) Assessment of COVID-19 data reporting in 100+ websites and apps in India. PLOS Glob Public Health 2(4): e0000329. https://doi.org/10.1371/journal.pgph.0000329

Editor: Prashanth Nuggehalli Srinivas, Institute of Public Health Bengaluru, INDIA

Received: August 9, 2021; Accepted: March 14, 2022; Published: April 15, 2022

Copyright: © 2022 Vasudevan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Our curated dataset used in this study are publicly available at https://github.com/varun-vasudevan/CDRS-India/tree/master/study3_june_2021 .

Funding: James Zou is supported by discretionary funding from Stanford University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors declare that there are no competing interests.

Introduction

How many people of each gender have died due to COVID-19 in India? What adverse events have been reported following vaccination? Which states are reporting the number of vacant oxygen beds in their hospitals? Such questions have strong public health implications. Is data reporting from the national and subnational governments in India granular enough to answer such questions? We answer that in this paper by documenting and analyzing the reporting of surveillance data [ 1 ], vaccination monitoring data [ 2 ], and bed availability during the second wave of COVID-19, focusing on granular information. Age-gender distribution for cases and deaths, adverse events following immunization stratified by vaccine and event type, and number of vacant oxygen beds are few examples of such granular information. Reporting granular COVID-19 data is important for the following reasons.

• It enables public health personnel to track the disease spread, vaccination, and adverse events across different sub-populations [ 2 , 3 ]. It also allows researchers to gain new insights, and to scrutinize the data to understand the rationale behind the policies put forth by the government.
• Granular data is also more transparent and informative for the general public. Governments cannot give personalized health recommendations to each citizen. The government’s advice on mask mandate, lockdowns, and vaccination is designed as a standard recommendation for all the citizens. However, health is a personal matter. If a person is more susceptible to COVID-19, then they need data on age, gender, comorbidities, AEFI (adverse events following immunization), etc., to make informed decisions.
• States in India are not isolated independent regions. People move from one state to another for work and leisure. Therefore, state governments should not just collect data and use them internally, but they should also publish the data so that anyone within the country can use them to make informed decisions.

Our assessment of reporting quality of surveillance, vaccination, and vacant bed availability data is timely and important for the following reasons.

• Several articles continue to mention the lack of surveillance data from India [ 3 – 5 ]. Therefore, it is necessary to understand and document what, where, and how much data is missing.
• Assessing the reporting of vaccination monitoring data informs how India fares now and the improvements necessary to overcome future vaccine hesitancy challenges.
• The second COVID-19 wave is significantly larger than the first and led to a severe shortage of resources like oxygen beds [ 6 ]. Therefore, it is important to know if the surveillance reporting adapted to the worsening pandemic [ 7 , 8 ] and if there was reporting on the resources that were in shortage.

Recent studies have highlighted that the official reports in India could be undercounting the true number of COVID-19 cases and death, which raises substantial public health challenges [ 3 , 9 , 10 ]. This work focuses on the complementary question of among the data that is reported, whether useful granularity is provided.

Between 22 May and 5 June 2021, we assessed digital platforms hosted by the national and subnational (state and union territory) governments for reporting data on COVID-19 surveillance, vaccination monitoring, and bed availability. Here, digital platforms refer to websites, and mobile applications designed for Android / iOS. We first checked the MyGov mobile app and CoWIN dashboard hosted by the Indian national government to report surveillance and vaccination monitoring data [ 11 , 12 ]. For each subnational, we then checked their government and health department websites/apps and performed a google search. Overall, we assessed more than 100 digital platforms. At least two authors checked each platform independently on different days and arrived at a consensus on what data is being reported. The complete list of digital platforms that we shortlisted and checked have been made publicly available through the dataset released with this paper. See Text A in S1 Text for more details on data curation.

Surveillance reporting

Vasudevan et al. developed a framework with 45 indicators to evaluate the reporting quality of COVID-19 surveillance data [ 7 ]. We use those indicators in the current study. The indicators check for availability, accessibility, granularity, and privacy violations in the reporting of confirmed, deceased, recovered, quarantine, and critical/ICU (intensive care unit) COVID-19 cases. These five categories indicate possible stages that a susceptible individual can go through during the pandemic.

Availability indicators check for total, daily, and historical data; accessibility indicators check for ease of access and reporting in English; granularity indicators check for total data stratified by age, gender, comorbidity, and districts; and privacy indicator checks if privacy is violated by including personally identifiable information in the reporting. During the assessment all indicators except the following two are scored either a 0 or a 1. The privacy indicator is scored a -1 if there is a privacy violation, else it is scored a 1. The “stratified by comorbidities indicator for deaths” is assigned a score of 1 if binary stratification (presence/ absence of comorbidity) of total deaths is reported. An additional score of 1 is given if more data such as stratification by a list of comorbidities or patient specific comorbidities are reported. We calculate two normalized scores for each subnational as described in [ 7 ]. One, a surveillance reporting score, which is the ratio of the total score earned by the subnational from all indicators and the maximum score possible from all indicators. Two, a granular surveillance reporting score, which is the ratio of the total score earned by the subnational from granular indicators and the maximum score possible from granular indicators. Both scores range between 0 (low) and 1 (high). During the calculation, the denominator is adjusted if any indicator does not apply to the subnational. For example, stratified by districts does not apply to Chandigarh because it does not have districts. Note that in [ 7 ], the surveillance reporting score is referred to as COVID-19 data reporting score and granular surveillance reporting score is referred to as granularity score.

To get a handle on the scale of missing granular data, we narrow our focus on the reporting of age and gender for confirmed cases; and age, gender, and comorbidity for deaths. Among subnationals reporting these items, some disaggregate the cumulative numbers by the items; some disaggregate the daily numbers by the items, and the remaining report the items for each individual. Considering all subnationals that report one or more of age, gender, and comorbidity, in any of the three forms mentioned above, we calculate the percentage of cases and deaths for which age, gender, and comorbidity distribution is available as of 5 June, 2021.

Vaccination reporting

Disaggregated monitoring of vaccination is essential to measure the progress and effectiveness of India’s vaccination campaign [ 2 ]. We developed a minimal set of indicators to assess the reporting quality of vaccination monitoring data. The indicators reflect recommendations from WHO and LANCET COVID-19 Commission India Task Force [ 2 , 13 ], and the vaccine operational guidelines from the Ministry of Health and Family Welfare (MoHFW) of India [ 14 ].

Indicators are grouped into three dimensions and are as follows. Availability : Daily and total vaccination. Accessibility : Daily vaccination trend graphic. Granularity : 1) Total vaccination stratified by districts and eligibility category (health care workers, front line workers, age 45+, age 18–44). 2) Total AEFI (adverse events following immunization) stratified by vaccine type (Covishield, Covaxin, Sputnik V); and event type (severe, serious).

For all indicators, except the AEFI ones, we check if data is reported separately for each dose (first and second). MoHFW classifies AEFI into three types: minor (e.g., pain and swelling at the injection site, fever), severe (e.g., non-hospitalized cases of anaphylaxis, sepsis), and serious (e.g., deaths, hospitalizations) [ 14 ]. We check for reporting on severe and serious events. Trend graphics are used as an indicator because they are concise and make it easier to identify patterns. Eligibility category refers to the order of eligibility in which vaccines were rolled out in India.

Vacant bed availability reporting

When resources such as oxygen beds are in shortage [ 6 ], it is important to report their vacancy to reduce panic among people in need. Therefore, we checked if subnationals report the number of vacant ICU/oxygen/ventilator beds disaggregated by districts/hospitals.

The geographical variation in surveillance reporting scores is shown in Fig 1A . See Table A in S1 Text for each subnational’s score. The five number summary of the surveillance reporting score is, minimum = 0.33, first quartile = 0.39, median = 0.46, third quartile = 0.49, and maximum = 0.61. MyGov provides seamless access to total and daily numbers and trend graphics for confirmed, recovered, and deaths for each subnational [ 11 ]. However, granular information such as cumulative numbers stratified by districts, age, gender, or comorbidity, is unavailable on MyGov, as summarized in Fig 1B .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

(A) Map showing the variation in surveillance reporting score across India. The map was generated using Tableau Desktop software version 2020.2.1 and the boundary information for regions in India was obtained as shapefiles from Datameet ( http://projects.datameet.org/maps/ ). (B) Table indicating what surveillance data is being reported (or not) for each subnational on the MyGov app. (C) Table indicating the vaccination data reported (or not) for each subnational on the CoWIN dashboard.

https://doi.org/10.1371/journal.pgph.0000329.g001

Fig 2 lists subnationals in the decreasing order of their granular surveillance reporting score. The five number summary of the surveillance reporting score is, minimum = 0, first quartile = 0, median = 0.17, third quartile = 0.22, and maximum = 0.50. Scores from previous assessments are shown for comparison. The northeastern state of Nagaland scored highest by reporting granular data through weekly bulletins. They report cumulative cases and deaths disaggregated by age and gender and cumulative deaths disaggregated by comorbidities, as shown in Fig 3 . Nagaland also compares data from 2020 (first wave) with data from 2021 (second wave). In contrast, the lowest scoring subnationals report little or no granular data. As of 5 June 2021, age and gender distribution are available for less than 22% of cases and deaths, and comorbidity distribution is available for less than 30% of deaths. Subnationals that report both age and gender distribution for cases are: Nagaland, Odisha, Tamil Nadu, and Telangana. Similarly, subnationals that report both age and gender distribution for deaths are: Karnataka, Nagaland, Tamil Nadu, and Kerala. See Fig 4A–4D for a compact summary of granular data availability and Text C in S1 Text for additional details. Even now, some subnationals do not report data stratified by districts. The tabular dataset released with this paper provides a comprehensive summary of what data each subnational is reporting.

The scores from previous assessments (2020) are shown for comparison [ 7 , 8 ]. The table also shows which subnationals are reporting (or not) vaccination coverage stratified by eligibility category; AEFI stratified by vaccine and event type; and vacant ICU/oxygen/ventilator bed availability disaggregated by districts/hospitals.

https://doi.org/10.1371/journal.pgph.0000329.g002

https://doi.org/10.1371/journal.pgph.0000329.g003

(A) Shows the % of cases for which age and gender distribution are available. Each subnational reporting that data is represented by a colored rectangle whose width denotes the % of total cases in India reported in that subnational. (B) Shows the % of deaths for which age, gender, and comorbidity data distribution are available. Each subnational reporting that data is represented by a colored rectangle whose width denotes the % of total deaths in India reported in that subnational. (C) Table showing the variation in the format of reporting of age, gender, and comorbidity among the subnationals that are reporting those data for the deaths. (D) Subnationals that stopped reporting total data stratified by age, gender, comorbidity or district after either of the surveillance reporting assessments conducted in 2020 [ 7 , 8 ]. *See Text D in S1 Text for more details.

https://doi.org/10.1371/journal.pgph.0000329.g004

Privacy violations

Chandigarh and Haryana are violating privacy by including individually identifiable information in their reporting. Chandigarh continues to release a document ( http://chandigarh.gov.in/health_covid19.htm ) containing the name and address of people who have completed/under quarantine. The document is over a thousand pages with over 100,000 entries. Haryana is releasing a document ( https://haraadesh.nic.in/ ) containing the name, age, gender, and address of cases from the Jhajjar district (see Fig A in S1 Text ).

CoWIN, launched in 2021, is a cloud-based information technology solution for planning, implementing, monitoring, and evaluating COVID-19 vaccination [ 14 ]. CoWIN dashboard reports the following for each subnational, district, and dose. Daily and total vaccination numbers, and daily vaccination trend graphics. CoWIN does not report total vaccination stratified by eligibility category for each dose. For AEFI, CoWIN reports daily AEFI numbers and the cumulative percentage. The number of severe and serious events disaggregated by vaccine type is missing ( Fig 1C ). Only 14 out 36 subnationals report on their digital platforms the total vaccination stratified by eligibility category for each dose. They are Nagaland, Kerala, Odisha, Karnataka, Ladakh, Puducherry, Uttarakhand, Gujarat, Jharkhand, Madhya Pradesh, Mizoram, Punjab, Himachal Pradesh, and Manipur. Karnataka is the only subnational that is reporting the number of severe and serious AEFI cases. AEFI reporting stratified by vaccine type is absent on all subnational platforms. These findings are summarized in Fig 2 .

20 out of 36 subnationals report vacant bed availability by hospitals and frequently update them. They are Haryana, Tamil Nadu, Kerala, Karnataka, Puducherry, Uttarakhand, West Bengal, Andhra Pradesh, Chhattisgarh, Gujarat, Madhya Pradesh, Punjab, Telangana, Rajasthan, Bihar, Chandigarh, Delhi, Goa, Himachal Pradesh, Dadra and Nagar Haveli and Daman and Diu. These results are also summarized in Fig 2 . It is a commendable effort from these subnationals to ensure the effective utilization of resources. Other subnationals are either not publishing any data on vacant bed availability or are reporting the total/vacant number of beds without classifying them. We encourage these subnationals to be more granular in reporting.

This is the largest study of its kind to assess the quality of COVID-19 data reporting in India. We did a comprehensive assessment of 100+ national and subnational government digital platforms (web and mobile) to identify what is present and what is missing in the reporting of surveillance data, bed availability, and vaccination monitoring data.

Overall, the quality of surveillance reporting has improved since 2020. Median surveillance reporting score has increased from 0.26 in May 2020 [ 7 ] and 0.30 in July 2020 [ 8 ]. This increase is primarily due to the consistent availability of high-level surveillance data through MyGov. However, the reporting of granular information such as age, gender, and comorbidity continues to be poor.

Age and gender distribution is available for about 1 in 5 cases and deaths in India. Similarly, comorbidity distribution is available for about 1 in 3 deaths. That is a staggeringly low number for a country with more than 344 thousand deaths. Essentially, we do not know even the basic information about who is getting infected and who is dying. This limitation has important implications.

• It prohibits researchers from tracking age-gender specific trends, identifying high-risk subgroups, and validating hypotheses on infection fatality rates [ 3 , 5 ].
• It is difficult to understand the effect of the virus on the age group below 18 without data on the age distribution of cases, deaths, and ICU cases. This is important as schools are reopening in India.
• Number of new confirmed cases per 100,000 population per week and number of COVID-19-attributed deaths per 100,000 population per week are two primary indicators to assess the level of community transmission as per WHO. It is important to track these indicators at the district level because subnationals in India are big and health care facilities vary significantly within a subnational. Therefore, states should publish data at least at the district level.

Going further, disaggregating cases and deaths by vaccination status (fully/partially/not vaccinated) is also essential to estimate the vaccine effectiveness in various sub-groups. Maharashtra, the state with the most deaths, does not report the age, gender, and comorbidity distribution. Even among subnationals reporting granular data for deaths, differences in the format of reporting make it difficult for comparison. See Fig 4C for a visual summary of the differences in the format of reporting.

A few subnationals have discontinued reporting certain granular items since the assessments in 2020 ( Fig 4D ). We highlight three specific instances. First, Karnataka, the state with the best surveillance reporting in 2020 [ 7 , 8 ], is no longer publishing war-room bulletins that had age and gender data for cases. Second, Kerala has stopped reporting comorbidity for deaths. There are claims that Kerala is undercounting deaths by attributing a portion of them as death due to comorbidity [ 15 , 16 ]. The removal of comorbidity data could strengthen such claims. Third, Jharkhand, a model state for granular reporting in the initial months, stopped reporting age and gender data as the pandemic worsened. It is important to scrutinize these changes in reporting to understand the bottlenecks or motives that led to the changes.

On the one side, there is inadequate reporting of essential granular data like age and gender distribution. On the other side, personally identifiable information is being published by subnationals like Chandigarh and Haryana. The public health benefits of the personally identifiable information released by these subnationals are unclear. Data reported by the government should include only the information necessary for public health activities [ 17 ]. Reporting personal data can discourage people from cooperating with the government or lead to discrimination against specific people [ 18 ]. For example, it might be possible to infer religion from names of some people and target specific groups leading to communal violence. India has already seen communal violence in the context of COVID-19 [ 19 ].

The quality of surveillance reporting in India has been analyzed extensively in three studies, including the current one. The first two studies were during the first wave of COVID-19 (roughly 3 and 6 months into the pandemic) [ 7 , 8 ], and the current study was during the second wave (after 15 months). Two crucial lessons that we learned collectively from these assessments are as follows. First, subnational governments are unlikely to make much progress in granular surveillance reporting without an official guideline from the central government on what data they have to report publicly. In fact, without someone to hold the subnational governments accountable, they can even switch from good to poor reporting practices (e.g., Karnataka and Jharkhand). Second, while official documents from the government, including a recent white paper from NITI Aayog (Vision 2035: Public Health Surveillance in India) [ 20 ], embrace the importance of privacy, there is an evident lack of awareness about privacy among officials releasing surveillance data.

We make three comments about the reporting of vaccination monitoring data. First, through the CoWIN dashboard, anyone can access vaccination coverage data for all subnationals and districts. It is a remarkable feat for such a large country. Second, governments should at least report vaccination coverage disaggregated by eligibility category. In the coming months, more disaggregated reporting based on gender, pre-existing conditions (comorbidities, pregnancy), socioeconomic, rural-urban, and other equity factors are necessary to ensure no sub-groups are left behind [ 2 ]. Third, there is an urgent need for reporting AEFI by vaccine type, sub-population affected, gender, and severity. Detailed and transparent AEFI data can increase citizens’ confidence in vaccines, especially as these vaccines are still in the emergency use authorization phase [ 21 ]. A large part of the success of polio elimination in India can be credited to disaggregated program data and a robust AEFI reporting system.

A similar study performed by Rocco et al. evaluated the quality of COVID-19 surveillance data across 15 federal democracies, including India [ 22 ]. Their evaluation using 13 indicators found a statistically significant association between subnational data quality and critical public health system capacity indicators. Countries such as the United States, Canada, Belgium, and Germany that are known to have substantial public health capacity and infrastructure scored higher on data quality. In contrast, countries like Argentina, India, and Malaysia scored significantly below the median score.

Our study has several limitations that would be interesting to address in follow up research. One main analysis limitation is that we can not evaluate the effect of data reporting quality on the containment of the virus. Therefore, our results should not be interpreted as “good reporting means good control of the pandemic.” While transparent and timely reporting of data is necessary, it is not sufficient. As discussed in the introduction, there could be a substantial number of COVID-19 cases and deaths that are under-reported, and we do not quantify these in our analysis. Our assessment is restricted to national and subnational (state and union territory) platforms and does not include district platforms.

Conclusions

By not reporting granular details, governments are making a choice to make certain information invisible to the scientific community and the public. One interesting direction for future research is to explore what decisions shape how governments in India are reporting COVID-19 surveillance and vaccination monitoring data [ 23 , 24 ].

As researchers and health professionals, our goal here is to advocate for change through measurement. Through a semi-quantitative approach, we showed the specifics and magnitude of missing granular data across India. Our findings provide the largest and most recent evidence for lack of granularity in India’s COVID-19 data reporting. Governments in India should recognize the importance of reporting granular data and make it a priority before the next wave of COVID-19. As a start, we recommend reporting the following. First, age and gender distribution for cases and deaths, and comorbidities for deaths. Second, details of serious/severe AEFI cases. Third, vaccination coverage for each dose stratified by eligibility category.

Supporting information

S1 text. information on shortlisting digital platforms, surveillance reporting score for each subnational, calculating the amount of missing granular surveillance data, additional notes on figs 2 and 4 , suggestions on granular reporting of testing data, and privacy violation in the reporting from haryana..

https://doi.org/10.1371/journal.pgph.0000329.s001

Acknowledgments

We want to thank healthcare workers across the globe for their efforts during the pandemic. We also thank the members of India COVID SOS and the Stanford community for their support and insightful feedback on a version of the draft.

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Real-Time Statistics and Visualization of the Impact of COVID-19 in India with Future Prediction Using Deep Learning

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The COVID-19 pandemic has hit almost all parts of the world. Originating in Wuhan, China to spread all across the world, it is safe to say now that the pandemic has shocked the people. Despite all the advancements in Medicine and Science, it is quite frankly a realization to the world that a virus can rip apart everyone’s lives. The USA, being the worst impacted by the same, countries like India are experiencing the growth of the virus rapidly. From social distancing to living with the virus, we humans are finding out different ways to survive this pandemic and return to normalcy [ 1 ]. The importance of understanding the situation reels’ core to the lives of everyone. In this research, we are keeping update about the change in events, cases reported, people deceased, people recovered, contact for essential service, the current status of the state/country is essential to provide a system that people could depend on during the time of the pandemic [ 2 ]. We have devised a system that works on the core principle of providing real-time information to the people, by supplementing it with the state-wise report, national reports, essential services, and contacts provided by the state. This research also explains the power of AI/ML, which will be used to predict how the cases would progress in the coming days. The model used in the research details an ensemble approach combining the advantages of Convolutional Neural Network and LSTM to create a hybrid approach.

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Kiran, S.R., Kumar, P. (2021). Real-Time Statistics and Visualization of the Impact of COVID-19 in India with Future Prediction Using Deep Learning. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_56

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Geographic information system-based analysis of COVID-19 cases in India during pre-lockdown, lockdown, and unlock phases

The World Health Organization formally announced the global COVID-19 pandemic on March 11, 2020 due to widespread infections. In this study, COVID-19 cases in India were critically analyzed during the pre-lockdown (PLD), lockdown (LD), and unlock (UL) phases.

Analyses were conducted using geospatial technology at district, state, and country levels, and comparisons were also made with other countries throughout the world that had the highest infection rates. India had the third highest infection rate in the world after the USA and Brazil during UL2.0–UL3.0 phases, the second highest after the USA during UL4.0–UL5.0 phases, and the highest among South Asian Association for Regional Cooperation (SAARC) countries in PLD–UL5.0 period.

The trend in the number of COVID-19 cases was associated with the population density where higher numbers tended to be record in the eastern, southern, and west–central parts of India. The death rate in India throughout the pandemic period under study was lower than the global average. Kerala reported the maximum number of infections during PLD whereas Maharashtra had the highest numbers during all LD and UL phases. Eighty percent of the cases in India were concentrated mainly in highly populous districts.

The top 25 districts accounted for 70.99%, 69.38%, 54.87%, 44.23%, 40.48%, and 38.96% of the infections from the start of UL1.0 until the end of UL phases, respectively, and the top 26–50 districts accounted for 6.38%, 6.76%, 11.23%, 12.98%, 13.40%, and 13.61% of cases in these phase, thereby indicating that COVID-19 cases spread during the UL period. By October 31, 2020, Delhi had the highest number of infections, followed by Bengaluru Urban, Pune, Mumbai, Thane, and Chennai. No decline in the infection rate occurred, even in UL5.0, thereby indicating a highly alarming situation in India.

Introduction

World health organization (who) declaration.

On February 11, 2020, the WHO officially referred to the disease caused by novel coronavirus 2019 as COVID-19 and the Coronavirus Study Group of the International Committee advised the name of the virus as SARS-CoV-2 ( Di Ferrante et al., 1975 ). The WHO formally announced the global COVID-19 pandemic on March 11, 2020.

Coronaviruses have a single-stranded RNA structure and they can infect humans as well as a huge range of animals ( Kooraki et al., 2020 ). Four diverse genera of coronaviruses have been identified comprising Alphacoronavirus , Betacoronavirus , Gammacoronavirus , and Deltacoronavirus . The first two genera may have originated from mammals, particularly bats, whereas the latter two could have come from pigs and birds ( Harapan et al., 2020 ). Betacoronaviruses as well as other virus subtypes can cause severe disease and fatalities. SARS-CoV-2 is a member of the Betacoronavirus genus.

Millions of people were infected within a few months of the start of the COVID-19 pandemic, where hundreds of thousands died and millions lost their jobs due to various restrictions imposed across the world ( Harapan et al., 2020 ). COVID-19 is one of the most infectious diseases and it severely affects certain demographics such as senior citizens, infants, and people with serious health conditions. The only means of relieving the COVID-19 pandemic is developing an effective vaccine and distributing it equitably across the world, which demands urgent commitment and investment from countries and international organizations such as the WHO. Globally, the WHO has been tracking more than 170 candidate vaccines and more than 36 were in various phases of human clinical trials by October 31, 2020. Pharmaceutical firms in several countries have accelerated their processes to develop candidate vaccines for SARS-CoV-2.

India’s response

There have been many global hotspots for COVID-19 cases but India reported its first positive case on January 27, 2020. India is the largest democracy and the second most populous country throughout the world, and thus it highly vulnerable to outbreaks of infectious diseases. During the early stage comprising the pre-lockdown (PLD) period in India, COVID-19 cases were mainly reported from people with a recent history of international travel. COVID-19 screening for passengers was initiated at airports and symptomatic cases were isolated whereas asymptomatic travelers were advised to undergo home quarantine. Within a very short time, some government authorized laboratories started limited real-time polymerase chain reaction (RT-PCR) testing in India. The required reagents for laboratory diagnostics were rapidly purchased, distributed, and deployed across the country through centralized planning by the Government of India (GoI).

At the epidemic stage, the GoI and research community prepared to implement control and prevention measures for COVID-19 infections in India. On March 22, 2020, a countrywide voluntary public curfew was requested by the prime minister of India to make the whole population aware of COVID-19. On March 25, 2020, the GoI initiated a synchronized countrywide lockdown (LD) phase 1.0 (LD1.0) for 21 days to limit the movement of the entire population as a preventive measure ( Rai et al., 2020 , Chinazzi et al., 2020 ) and all services were suspended except essential services. Other preventive measures were also implemented across the country, such as wearing masks and hand gloves, using hand sanitizer, maintaining social distancing, and controlling mass gatherings, and the number of infections slowed significantly during LD1.0. On April 15, 2020, the GoI extended the LD period to LD2.0 for a further period of 19 days on the recommendation of state governments and other advisory committees of the GoI.

LD2.0 was imposed under the Disaster Management Act, 2005 (DM Act) to fight the COVID-19 pandemic. The DM Act allowed the GoI to take necessary measures to effectively manage the disaster situation in the country by coordinating with national/state agencies and international organizations. LD3.0 was further extended for a period of two weeks from May 4, 2020, and all 727 districts across the country were divided into three zones (green, orange, and red) depending on the infection rate. On May 18, 2020, LD4.0 was imposed for a further period of two weeks by the GoI. During the LD1.0–LD4.0 phases, the entire land transport system was stopped, industries were closed, air flights were cancelled, and public gatherings were completely suspended.

On June 1, 2020, due to the economic requirements of the country, the GoI announced some relaxations in the green and orange zones with the conditional resumption of limited services, and this resumption phase was referred to as unlock (UL) phase 1.0 (UL1.0). The second phase of UL i.e. UL2.0, was declared for July 1–31, 2020, with the further easing of restrictions. Limited international travel was also permitted in UL2.0 as part of the Vande Bharat Mission. UL3.0 was announced for the month of August 2020, and permission was given for the reopening of gymnasiums, yoga centers, and all inter-and intra-state travel. On August 29, 2020, the GoI issued guidelines for activities permitted in UL4.0 (September, 2020), where LD measures were continued in containment zones. Regular hand washing and other precautionary measures were made compulsory in public places, workplaces, and public transport. UL5.0 was announced for the month of October 2020 with further relaxation of movements and the opening of closed facilities. Educational institutions remained closed during the entire LD and UL periods.

In India, the use of public safety measures such as surgical face masks, single-use gloves, hand sanitizer, personal protection equipment kits, tissue papers, and other medical waste generated vast amounts of waste, which affected the environment ( Ficetola and Rubolini, 2020 ) as well as influencing COVID-19 transmission. According to government reports from the months of August 2020 ( CPCB, 2020a ), September 2020 ( CPCB, 2020b ), and October 2020 ( CPCB, 2020c ), India generated about 3025, 4253, 5238, 5490, and 5597 tons of COVID-19 related bio-medical waste in the months of June, July, August, September, and October during 2020, respectively. Moreover, the effects of the COVID-19 pandemic are not uniform and diverse socio-economic groups have been affected differently. Understanding the various consequences of the pandemic on different socio-economic groups is not a simple task ( Weston and Frieman, 2020 ). However, socio-economic groups with a poor economic status are clearly at a greater risk from the spread of the COVID-19 pandemic ( van Staden, 2020 ) in India. In the present study, COVID-19 cases in India were analyzed using geospatial technology during the PLD, LD, and UL phases imposed by the Indian government.

Utilization of geographic information system (GIS) data for understanding the spread of COVID-19 infections

Various mapping techniques have been employed to track and understand the spatial and temporal distributions of many infectious diseases, including cholera, and influenza, and for plague containment ( Sarfo and Karuppannan, 2020 ). GIS is an emerging global health tool for mapping and monitoring the spatial and temporal distributions of infectious diseases. Geographic information can play vital roles in tracking a pandemic, particularly in tasks such as spread identification, prevention and control, allocation of resources, and detecting social sentiment ( Pourghasemi et al., 2020 , Gatto et al., 2020 ) and responses during outbreaks ( Kamel Boulos and Geraghty, 2020 ). GIS can allow epidemiologists to map the present and past occurrence of diseases together with many other parameters representing the environment, geography, and demography. These data may help epidemiologists to understand the source of outbreaks as well as the spread pattern and intensity, thereby facilitating the implementation of appropriate disease control, preventive, and surveillance measures ( Hellewell et al., 2020 , Kamel Boulos and Geraghty, 2020 , Murugesan et al., 2020 , Papastefanopoulos et al., 2020 , Pourghasemi et al., 2020 , Rai et al., 2020 , Sarfo and Karuppannan, 2020 , Zhou et al., 2020 ). Similarly, policy makers, public health agencies, and administrators can employ GIS tools to understand the overall outbreak patterns in real-time to identify high-risk populations and intervene accordingly by evaluating existing facilities or creating new healthcare infrastructure.

In India, GIS technology is implemented widely and many Web-GIS-based dashboards have been developed for COVID-19 data visualization ( Kamel Boulos and Geraghty, 2020 , Pourghasemi et al., 2020 , Rai et al., 2020 , Salgotra et al., 2020 ). COVID-19 is characterized by large-scale infection, a longer incubation period, and undefined detection, (no specific symptom(s) for detecting COVID-19), thereby resulting in urgent requirements for scientific and technological support to control and prevent the spread of the pandemic ( Hellewell et al., 2020 , Rahman et al., 2020 ). During the COVID-19 pandemic, the GIS community developed rapid methods for the visualization of COVID-19 information, spatial tracking of infected cases, and resource planning and management to satisfy basic public needs ( Zhou et al., 2020 ). GIS has been actively used by international bodies such as the WHO as well as academics and local governments for communicating essential information to the public regarding the COVID-19 pandemic. Previous studies have conducted GIS-based disease outbreak, risk, and infection behavior analysis for COVID-19 cases using different mathematical and statistical models ( Ficetola and Rubolini, 2020 , Gatto et al., 2020 , McBryde, 2020 , Murugesan et al., 2020 , Papastefanopoulos et al., 2020 , Pourghasemi et al., 2020 , Salgotra et al., 2020 , Zhou et al., 2020 ).

Study region and data sources

India is a part of the Asian continent and it is situated between 8°4' N to 37° 6' N latitude and 68° 7' E to 97° 25' E longitude, with a total population of 1,210,854,977 according to Indian census data from 2011 and a total land area of 3,287,590 km 2 . India is the seventh largest country by area throughout the world and the second most populous country. India has a federal structure with 28 states and nine union territories. The major cities in India are Delhi, Mumbai, Chennai, Kolkata, Bengaluru and Hyderabad. Currently, 34 international airports operate in India.

COVID-19 data were collected for diagnosed infected cases and recovered cases, and the total deaths in India during the period from January 30 to October 31, 2020. Daily data for cumulative confirmed, recovered, and death (CRD) cases were obtained from the “covid19india” website ( https://www.covid19india.org ). Census data were obtained from the Office of the Registrar General and Census Commissioner of India, ( https://censusindia.gov.in/2011census/population_enumeration.html ). Data regarding confirmed and death cases for other countries were downloaded from the WHO coronavirus disease dashboard ( https://covid19.who.int/ ). Other data were collected from peer reviewed research studies on COVID-19 in India and other parts of the world.

Results and discussion

The spatial and temporal distributions of COVID-19 infections in India during the PLD and various LD and UL phases imposed by the GoI to fight the COVID-19 pandemic are described in the following.

Current status in India

On October 31, 2020, a total of 2121 operational laboratories reported to the Indian Council of Medical Research for COVID-19 testing in India ( ICMR, 2020 ). Among these 2121 operational laboratories, 1145 laboratories conducted independent RT-PCR testing, 848 laboratories were authorized for the TrueNat Test, and 128 laboratories conducted the CBNAAT Test for COVID-19 ( ICMR, 2020 ).

The total number of confirmed COVID-19 cases in India exceeded 8.1 million by October 31, 2020. Table 1 shows the numbers of COVID-19 cases in India during the PLD, LD, and UL phases.

Summary of COVID-19 cases in India by October 31, 2020.

Bold values show the critical values of respective parameter at which remarkable changes had happened.

The number of COVID-19 tests increased from the PLD to UL5.0 phases, and the number of CRD cases increased from the PLD to UL4.0 phases but decreased in the UL5.0 phase. The positivity rate was highest in UL2.0 and it decreased subsequently in UL3.0–UL5.0. The recovery rate was lowest in the PLD period but it then increased to 92.04% in the UL5.0 phase. Initially, the death rate was 1.73% in the PLD period and it then reached a maximum of 3.27% in LD1.0, before decreasing to 1.49% in UL5.0. No declines occurred in the number of infected persons per million population (infection rate), even in UL5.0, which is a highly alarming situation for India.

Figure 1 shows the cumulative COVID-19 cases in the districts of India by October 31, 2020. The highly infected districts were in the southern, eastern, and west–central parts of the country. By October 31, 2020, 8,137,119 confirmed cases (17.87% of global infections), 7,489,426 recovered cases, 121,641 deaths (10.23% of global deaths), and 526,052 active cases had been recorded in India.

Cumulative COVID-19 cases in the districts of India by October 31, 2020: (a) confirmed cases, (b) recovered cases, (c) deaths, and (d) active cases.

Delhi, Mumbai, Chennai, Kolkata, Hyderabad, and Bengaluru recorded the highest numbers of cumulative confirmed cases per unit area, and the maximum recoveries per unit area were reported in DL, Mumbai, Chennai, Kolkata, Bengaluru, and Chandigarh. By the same date, Mumbai, DL, Kolkata, Chennai, Chandigarh, and Bengaluru had recorded the most deaths, and Hyderabad, DL, Mumbai, Kolkata Chennai, and Kamrup Metropolitan had the highest numbers of active cases per unit area.

The spatial distributions of the cumulative confirmed, recovered, death, and active (CRDA) cases in the states of India were also analyzed. At the end of UL5.0, Maharashtra (MH) followed by Karnataka (KA), Andhra Pradesh (AP), Tamil Nadu (TN), and Uttar Pradesh (UP) states had the highest numbers of cumulative confirmed cases (55.38% of the total cases), and MH followed by AP, KA, TN, and UP had the largest numbers of recovered cases (56.1% of total recoveries). The maximum numbers of deaths were reported in MH, KA, TN, UP, and West Bengal (WB) (65.55% of total deaths), and the highest number of active cases are recorded in MH, Kerala (KL), KA, WB, and Delhi (59.55% of total active cases).

The cumulative confirmed cases, deaths, and death rates in India are compared with the global values at the end of the PLD, LD, and UL phases in Table 2 . The death rate in India was lower than the global average during the entire study period. The highest death rate was 3.27% in India during the middle of April 2020, but the highest average global death rate was 7.30% ( Table 2 ) at the start of May 2020.

Cumulative COVID-19 cases and death rates (%) in India and globally.

COVID-19 situation in India during PLD, LD and UL phases

The total CRD cases due to the COVID-19 pandemic in India during the PLD, LD, and UL phases are shown in Table 2 and Figure 3 . Figure 2 shows the spatio-temporal distribution of confirmed cases in the states of India. In the PLD period, the highest case numbers were reported in KL followed by MH, KA, Telangana (TG), and UP. The numbers of confirmed cases increased ( Figure 2 ) in almost all states during the LD phases. The top five states (in chronological order) with the highest numbers of confirmed cases in each of the LD phases were: (1) LD1.0: MH with 2537 cases and acted like a epicenter, followed by DL, TN, Rajasthan (RJ), and Madhya Pradesh (MP); (2) LD2.0: MH, Gujarat (GJ), DL, MP, and UP; (3) LD3.0: MH, TN, GJ, DL, and RJ; and (4) LD4.0: MH, TN, DL, GJ, and RJ.

Confirmed COVID-19 cases during PLD, LD, and UL phases.

COVID-19 cases in India during LD and UL phases: (a) cumulative cases, (b) daily cases, and (c) deaths.

A rapid spatial spread of confirmed COVID-19 cases was observed in MH, TN, and DL during UL1.0 ( Figure 2 (b)), and it continued in other states such as KL, AP, and KA in the south, Punjab (PB) and UP in the north, and WB, Odisha (OD), and Bihar (BR) in the east until UL4.0 ( Figure 2 (c), (d), and (e)). Figure 2 (f) shows that slight decreases in the numbers of confirmed cases occurred during UL5.0 in all states. Infections increased rapidly due to the relaxation of interstate movements and the opening of market shops, business offices, industries, religious places, etc. Rapid increases in COVID-19 cases were observed in areas with high population densities, poor testing facilities without frequent and adequate testing, and changes in human behavior due to various reasons. The top five states with the maximum numbers of cases in all UL phases were: (1) UL1.0: MH, TN, DL, GJ, and UP; (2) UL2.0: MH, TN, AP, KA, and UP; (3) UL3.0: MH, AP, KA, TN, and UP; (4) UL4.0: MH, KA, AP, TN, and UP; and (5) UL5.0: MH, KL, KA, AP, and TN. MH was the epicenter for COVID-19 infections in India and the first reported confirmed cases occurred in this state during all LD and UL phases. Due to the efforts of the GoI, many testing sites were distributed evenly across states irrespective of the population density and healthcare facilities, and thus the infection rates and rate of spread could have been lower in the Indian states, as reflected in UL5.0.

Figure 3 shows the spread of COVID-19 where the infection rate was lower during the LD phases (LD1.0–LD4.0) but it increased significantly after the start of the UL phase on June 1, 2020. Overall mobility was restricted during the LD phases, before it was relaxed in a phased manner during the UL period. Thus, the measures implemented in different regions during LD to restrict movement helped to limit the infection rates. Figure 3 (a), (b), and (c) show the cumulative (confirmed, recovered, and active) cases, daily confirmed and recovered cases, and deaths (cumulative and daily) in India, respectively. The daily reported cases peaked during the middle of UL4.0 phase and reached their lowest level in UL5.0 phase.

Comparison of COVID-19 in India with selected countries across the world and South Asian Association for Regional Cooperation (SAARC) countries

The total confirmed COVID-19 cases in India were compared with those in the most affected countries throughout the world as well as in SAARC countries. Figure 4 (a) shows that India was the third most badly affected country in the word after the USA and Brazil during the UL2.0 and UL3.0 periods, and the second most badly affected after the USA during UL4.0 and UL5.0. India was the most badly affected among the SAARC countries during the entire study period ( Figure 4 (b)).

Cumulative confirmed cases in the most badly affected countries: (a) selected countries, and (b) SAARC countries.

Classification and distribution of 80% of confirmed cases

The infection spread rate was not uniform throughout India during the entire pandemic period. The maximum infection rates were concentrated around high population density areas. Figure 5 (a) shows a population map of the districts of India, with the very high population areas in MH, WB, AP, DL, UP, and BR. Confirmed COVID-19 cases were classified at the end of each phase based on the percentage contribution of each district in the whole country. The totals of the red dots in Figure 5 (b)–(f) represent 80% of the confirmed cases in a district, which indicate that uniform government control was not required in all districts. In order to save resources and control the spread of the pandemic in a timely manner, the government focused on the locations that comprised 80% of the cases. At the end of UL1.0, the number of these locations was low with 74 districts but it subsequently increased at the end of UL2.0–123 districts, and then to 168 locations in UL3.0, 187 districts in UL4.0, and 189 districts in UL5.0, as shown in Figure 7 (a). Figure 5 indicates that the distribution of the districts that contributed 80% of the cases was greater in the most populous regions of the country.

Classifications and distributions of cumulative confirmed COVID-19 cases, where the total of the red dots represents 80% of the cases in a district.

Contributions of top districts to COVID-19 confirmed, recovered, death, and active cases over 15-day intervals: (a), (c), (e), and (g) number of districts that accounted for 80% of total CRDA cases, respectively; (b), (d), (f), and (h) percentage contributions of top districts to CRDA cases, respectively.

Percentage contributions of Top 25 and Top 26–50 infected districts

The results also showed that only a few districts accounted for most of the total confirmed cases in India. The percentage contributions of the top 25 districts, top 26–50 districts, and remaining 677 districts in different time periods are shown in Figure 6 . On June 1, 2020 immediately after the LD4.0 period, 70.99% of the confirmed cases occurred in the top 25 districts, 6.38% of confirmed cases in the top 26–50 districts, and only 22.63% of the confirmed cases in the remaining 677 districts. After the UL started, the contributions of the top 25 districts decreased, whereas those of the top 26–50 districts increased slowly and those of the remaining districts increased rapidly, thereby clearly demonstrating that the spread of COVID-19 cases increased during the UL period, as shown in Figure 6 , Figure 7 (b).

Percentage contributions of top 25 districts, top 26–50 districts, and remaining districts to confirmed cases.

Figure 7 shows the number of districts that accounted for 80% of CRDA cases, as well as the percentage contributions of the top 25 districts and top 26–50 districts in India to CRDA cases at 15-day intervals. The number of districts that accounted for 80% of CRD cases increased in the UL phases ( Figure 7 (a), (c), and (e)), whereas the number of active cases increased until the middle of UL4.0 and then decreased ( Figure 7 (g)). The percentage contributions of CRD cases ( Figure 7 (b), (d), and (f)) by the top 25 districts and top 26–50 districts were highest in UL1.0 before they then decreased, thereby demonstrating the greater spatial spread of CRD cases in the remaining districts. The contribution of the top 25 districts to active cases ( Figure 7 (h)) was around 74% in UL1.0 but it then decreased to 41% at the end of UL4.0, before increasing again in UL5.0. The decrease in the number of districts that accounted for 80% of active cases and the increase in the contribution from the top 25 districts at the end of UL5.0 show that the recovery rate was very high in other districts.

COVID-19 cases in selected districts

Throughout the LD and UL phases, the maximum numbers of cases were reported for only a few major cities in India. Figure 8 shows the confirmed, death, and recovered cases at the start of UL1.0 and during each UL phase in major Indian cities. During UL5.0, Bengaluru Urban had the highest number of confirmed cases ( Figure 8 (a)) and highest number of recovered cases ( Figure 8 (c)), whereas Mumbai had the lowest maximum number of COVID-19 infected persons ( Figure 8 (b)).

COVID-19 cases in selected districts of India during UL phases: (a) confirmed cases, (b) deaths, and (c) recovered cases.

Figure 9 shows the numbers of COVID-19 CRDA cases in the top 25 districts at the end of UL5.0. DL had the most confirmed cases followed by Bengaluru Urban, Pune, Mumbai, Thane, and Chennai.

Top 25 districts in India in terms of confirmed COVID-19 cases by October 31, 2020: (a) confirmed and recovered cases, and (b) active cases and deaths.

COVID-19 cases in Indian states

The COVID-19 CRD cases in Indian states during the PLD, LD, and UL phases, as well as the CRDA cases at the end of UL5.0 are shown in Figure 10 . The total numbers of CRD cases were highest in MH during the PLD, LD, and UL phases, mainly because major cities such as Mumbai, Pune, Thane, Nagpur, and Nashik are located in this state. At the end of UL5.0, MH still had the most CRDA cases compared with other states, as shown in Figure 10 (d) and (e).

COVID-19 cases in Indian states: (a) confirmed cases during PLD, LD, and UL phases; (b) recovered cases during PLD, LD, and UL phases; (c) deaths during PLD, LD, and UL phases, (d) confirmed and recovered cases by October 31, 2020; and (e) active cases and deaths by October 31, 2020.

Conclusions

In this study, geospatial analysis was conducted to assess COVID-19 CRDA cases in India during the PLD, LD, and UL phases. Analyses were performed at the country, state, and district levels, and Indian COVID-19 cases were also compared with those in the other most infected countries throughout the world. The death rate in India was lower than the global average throughout the pandemic period considered. India was the third most badly affected country throughout the world after the USA and Brazil during the UL2.0 and UL3.0 periods, and the second most badly affected after the USA during UL4.0 and UL5.0. India was the most badly affected of SAARC countries from the start of the pandemic until UL5.0. In India, the highest positivity rate was 8.47% in UL2.0, the highest death rate was 3.27% in LD1.0, and the highest recovery rate was 92.04% in UL5.0. The total daily numbers of confirmed and recovered cases in India peaked during the middle of UL4.0, before subsequently decreasing until the end of UL5.0.

The spatial distributions of confirmed cases in Indian states were mapped during the PLD, LD, and UL phases. The numbers of confirmed cases increased in all states from PLD to UL4.0, but decreased slightly in UL5.0. The maximum number of confirmed cases during PLD occurred in KL, and the highest numbers of confirmed cases during all LD and UL phases were recorded in MH. Major increases in COVID-19 cases during UL1.0–UL4.0 were observed in the west–central part comprising MH, southern states of KL, TN, AP, and KA, northern states of Delhi, PB, and UP, and eastern states of WB, OD, and BR.

The observed infection spread rate was not uniform throughout the country during the entire pandemic period considered. Classifications of confirmed cases at the end of each UL phase based on the percentage contribution of each district in the whole country showed that 80% of the confirmed cases occurred in a few high population areas, i.e., MH, AP, TN, KA, KL, GJ, DL, UP, BR, WB, and OD. This spatial distribution could facilitate decision making when controlling the spread of COVID-19. The results showed that among 727 districts in India, 80% of the confirmed cases occurred in only 74, 123, 168, 187, and 189 districts at the end of each UL period, respectively. UL1.0: June 1-30, 2020, UL2.0: July 1-31, 2020, UL3.0: August 1-31, 2020, UL4.0: September 1-30, 2020, UL5.0: October 1-31, 2020. In addition, the contributions of the top 25 districts, top 26–50 districts, and remaining districts to CRDA cases were assessed. The top 25 districts accounted for 70.99%, 69.38%, 54.87%, 44.23%, 40.48%, and 38.96% of cases at the start of UL1.0 and at the ends of UL1.0–UL5.0, respectively, and the top 26–50 districts accounted for 6.38%, 6.76%, 11.23%, 12.98%, 13.40%, and 13.61%. Clearly, the contributions of the top 25 districts decreased when UL started, whereas those of the top 26–50 districts increased slowly and those of the remaining districts increased rapidly, thereby indicating the greater spread of COVID-19 cases in India during the UL phases. Moreover, the top 25 districts accounted for around 74% of the active cases in UL1.0, before decreasing to 41% at the end of UL4.0 and then increasing again in UL5.0. By October 31, 2020, DL had the highest number of confirmed cases in India followed by Bengaluru Urban, Pune, Mumbai, Thane, and Chennai.

Conflict of interest

This is to inform you that the proposed reviewers have no conflicts of interest to declare and they agreed to review this manuscript. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding source

No funding source was associated with this COVID-19 related study.

Ethical statement

The instructions to the authors have been read and accepted by all the authors of this manuscript. All of the authors have seen the manuscript and agreed to the submission of this version. The research described in this paper is manuscript and it has not been published or submitted for publication in any other journal simultaneously. We also agree that if this paper is accepted for publication, the paper will not be published elsewhere in the same form in English or any other language without the written consent of the copyright holder.

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• Research article
• Open access
• Published: 31 March 2021

An analysis of COVID-19 clusters in India

Two case studies on Nizamuddin and Dharavi

• Pooja Sengupta   ORCID: orcid.org/0000-0002-7859-2435 1 ,
• Bhaswati Ganguli 2 ,
• Sugata SenRoy 2 &
• Aditya Chatterjee 2  

BMC Public Health volume  21 , Article number:  631 ( 2021 ) Cite this article

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In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread.

A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments.

The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control.

Conclusions

The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

Peer Review reports

The Corona Virus Disease 2019 (COVID-19) caused by the novel corona virus SARSCoV-2, started at Wuhan in the Hubei province of China and has spread with great speed around the world. More than a year has passed since and the virus is still wreaking havoc in many nations including especially the US and India. The objectives of the analysis presented in this paper are as follows:

In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals.

Simulation using a compartment model is used to provide insight into differences in response to public health interventions.

Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread of COVID-19.

As COVID-19 pandemic aggressively spreads across continents, vigorous public health responses are now being put in place in all the countries hit by the virus. Articles are being added to the expanding body of work on COVID-19 in f l a t t e n i n g t h e c u r v e . Many recent research papers have discussed the assessment of these containment policies through prediction of the path of COVID-19 cases in India, for different scenarios [ 1 – 5 ]. Some recent papers have discussed in detail the outbreak of the disease across the globe. The main underlying theme of most of these studies is to track and predict the path of the virus spread in the nation. Researchers have toiled to predict the time it will take the outbreak to subside. As a result different groups of researchers have made use of various modelling techniques for prediction and have come up with many interesting findings. For example, [ 6 ] have revealed that a reduction in the contact rate between uninfected and infected individuals by quarantining the susceptible individuals, can in effect reduce the basic reproduction number. Now, with the advent of the COVID-19 outbreak, governments all over the world have started enforcing interventions like increased quarantining, self-isolation, increased hospital facilities etc. In this paper we have made an endeavour to follow the path of the disease and predict the prevalent number of susceptible, infected, recovered, and fatal cases for the Indian population cohort. This is done through some simulations with the timely government interventions being put in place. Essentially the simulation helps us predict prevalence of the infections, numbers recovered and fatal cases through simulations using the SEIQHRF epidemic model, by moderating the parameters of the model according to the government interventions and emergency policies.

In this study we have performed a cluster analysis on the worst hit districts of India. With variables like number of confirmed COVID-19 cases, number of COVID-19 hospitals, and population density of the respective districts we have tried to classify different districts in India according to their homogeneity in nature due to these factors. The main objective of clustering in this study is to improve monitoring of the affected areas which will be useful in understanding seriousness of the spread of novel coronavirus (COVID-19) to revamp government policies, decisions, medical facilities (ventilators, testing kits, masks etc.), treatment etc. This in turn will help to reduce number of infected and deceased individuals. A similar clustering approach is used in a study carried out by [ 7 ].

In addition to clustering, a scenario analysis of the respective areas facilitate following the growth curve in the respective states. For that purpose, individuals have been divided into various groups, such as, susceptible, exposed, infected, infectious (but self isolated), hospitalized, recovered and dead (death but not hospitalized, from COVID-19). Various parameters used in the probabilistic model are, the number of per day exposure events ( act ) between infectious and susceptible individuals; chance of iinfection being spread at each exposure event between the infectious and susceptible; rate at which the symptomatic self isolate themselves; the per day rate of symptomatic people requiring hospitalisation; per day rate at which people requiring hospitalisation recover; and per day fatality rate for people needing hospitalisation but could not because the full capacity of the hospitals was occupied. Each of these groups of individuals are divided into these various compartments, and the transition rates of individuals into and out of these compartments are also taken into consideration in the model. The purpose of this method used is to look at different intervention experiments and follow the prevalence of COVID-19 (in terms of number of persons) with each passing day since the beginning of the epidemic. The research has been carried out for some of the Indian states where we find the maximum number of districts with high number of confirmed COVID-19 cases. The act parameter in this study, is adjusted for each state based on its population density. Thus, we follow the prevalence curve of the cohorts from the chosen states and evaluate the effectiveness of the interventions imposed by the government to curb the COVID-19 growth.

The rest of the paper is organized as follows: “ Methods ” section describes the SEIQHRF epidemic model and explains the parameters involved and explains the two clustering techniques used to classify districts. “ Results ” section reports the district-wise cluster analysis results. “ Discussion ” section discusses the case of Nizamuddin basti in Delhi,where a religious gathering exposed thousands to this virus and caused a second wave of infection in states when Nizamuddin attendees returned home. Here we discuss the results from fitting the SEIQHRF model. This section also discusses the case of Dharavi, one of Asia’s largest slums with very high population density. “ Conclusion ” section enumerates the findings of this study and concluding remarks.

Two of the most commonly used algorithms in clustering are the hierarchical and the k-means algorithm. In this study we have used both these techniques of clustering technique due to their easily interpretable visualization and intuitive interpretation. One of the most relevant characteristics of hierarchical clustering is the fact that the user does not need to provide the number of clusters beforehand. Based on the variable upon which clustering is done, the observations are clustered into optimum number of groups. However a disadvantage of the method is that it has a high computational complexity when the number of observations to classify is very high. Thus using it on all 640 districts in India is quite cumbersome. So we have chosen the top 50 worst affected districts with maximum number of confirmed COVID-19 cases. The methodology used to build the hierarchical clustering, as also used in [ 8 ], is the following:

Pairwise distance between all the hotspot districts is calculated using D i ( S A , S B ), for a particular value of the parameter α . This distance matrix is symmetrical and has a null diagonal, which will be important to analyze the similarity between the different districts.

Searching through the distance matrix, we select the two most similar two districts.

These two districts are joined then to produce a new group that now has at least two districts.

The distance matrix is then updated by calculating the distances between the new cluster and all other clusters.

Step 2 is repeated until all districts belong to a group.

The k-means algorithm is one of the most popular clustering technique that divides the data samples into pre-defined distinct subgroups, where each data point belongs to just one group. It keeps data points within a cluster similar to each other and also tries to maximize the heterogeneity between two clusters. A cluster refers to a set of information aggregated together owing to certain similarities. Unlike hierarchical clustering in k-means the number of cluster is prefixed. The optimum number of clusters, say k , is obtained by using an elbow plot. From the dataset the method identifies k centroids, and then assigns every data point to the cluster it is closest to, keeping the centroids as small as possible. The clustering stops when either:

The centroids have more or less become constant and there is no change in their values.

The number of iterations defined has been reached.

In our study, data on confirmed COVID-19 cases, population density and number of COVID-19 hospitals in each districts is used to get the set of centroids. Although before the k -means clustering is performed the variables were standardized so that they are all on the same scale.

SEIQHRF model

One of the popular models used for predicting the course of an epidemic such as COVID-19, is the Susceptible-Infectious-Recovered (SIR) model ([ 9 – 11 ]), [ 12 ]. In this model, the population is segregated into three separate compartments, i.e. Susceptible (S), Infectious (I) and Removal (R), where transition rates between the compartments is defined beforehand. Figure  1 is a representation of the three compartments in SIR model. In order to describe the per day rate at which individuals move in or out of the compartments, two equations are used. It is by solving these equations that we get the prevalence in each compartment.

SIR model flowchart- This figure represents the different compartments of the model and the arrows represent the direction of flow of individuals from one to the other. The SIR model predicts the prevalence of susceptible, infected and recovered individuals

The SEIR model, a variant of the SIR model, is one which includes a new compartment called Exposed (E), [ 13 – 15 ]. However, more recently an extension of the SEIR model has been proposed by [ 4 , 16 ], where apart from the three compartments, Susceptible (S), Infectious (I), Exposed (E) and Removal (R), two other compartments are included. These compartments are Quarantine (Q) and Hospitalization (H). The compartment H is supposed to take into account the healthcare capability of a state/district. In this new model the Removal (R) stage is divided into Recovery (denoted by R) compartment and fatality (denoted by F) compartment. The dynamic nature of the model is due to the fact that the number of individuals in each compartment may change over time. The flowchart showing the various compartments in the model and the arrows depict the direction of movement of individuals into and out of them is shown in Fig.  2 [ 16 ]. The following Table  1 , provides a description of individual compartments.

SEIQHRF model flowchart-This figure represents the various compartments and the arrows represent the direction of movement of individuals from one to the other

With the introduction of more compartments into the SIR and SEIR models, the systems of equations that should be solved to get the prevalence, become very complicated. Thus, in case of SEIQHRF, probabilistic compartment models are developed. Probabilistic models help simulate individuals in a population, which allows them to move between compartments and make it easy to specify various probabilities of passing on infection between the individuals in the different compartments, such as moving from infectious to susceptible. The probabilistic model can be further improved to adequately model real-life scenarios. We have thus used the model to explore the effects numerous public health interventions and policies through scenario analyses, [ 5 ].

The extensions of SEIR model adds a number of compartments in the analysis. The E compartment is for the individuals exposed to the virus and infected with it, but are still asymptomatic with the potential of becoming infectious. This is also true for the traditional SEIR models, but the normal assumption is that the asymptomatic infected do not spread the infection to others. But that condition was relaxed in [ 16 ] due to increasing evidence in support that most infections are being spread during the incubation days when the carrier is asymptomatic. This evidence also supports the flowchart in Fig.  2 , where there is a movement of individuals from the I infected symptomatic compartment to Q , quarantine compartment. But the individuals in the E compartment only move to the Q compartment when they have gone through compartment I i.e. only when they have shown some symptoms of the infection.

Similarly, the H compartment represents those that need hospitalisation. There’s a parameter h o s p . c a p that specifies the hospital capacity, and it is possible to indicate increased mortality rates for those who need hospitalisation in the H compartment but cannot get such care because the total h o s p . c a p has been exhausted. Related to this, the F compartment shows the case fatalities; i.e. the deaths in COVID-19 cases due to the virus. There are also some parameters for background death rates due to other causes among the susceptible (defined as the crude death rate for India). Also note that case fatalities are restricted only to occur among those in the H compartment, that is, those requiring hospitalisation (irrespective of whether they can get it or not). A similar method for modelling spread of COVID-19 has been used by [ 5 ].

In the present study we make an endeavour to fit the SEIQHRF model mentioned in Fig.  2 to COVID-19 outbreak data for some states in India where COVID-19 has been rampant. The purpose of this model is two-fold. First, the resulting simulations are used to study in detail the impact of lockdown on the epidemic. Second, we perform a what -if analysis for strategies to impose and relax lockdown at the most opportune time.

Choice of parameters

The timeline used for this study is the beginning of the second phase of lockdown in India, primarily because the spread of cases from the Nizamuddin cluster became prominent towards the beginning of April and the first case in Dharavi slum was also reported on the 1st of April. Thus using 15th April, the start of lockdown phase 2 in India as the starting point of this study. We obtain the best fit to the observed cases and fatality data of COVID-19 by using the following set of parameters, which are represented in Table  2 .

The starting day for the simulation was taken as 15th April 2020 (when second phase of lockdown began). The initial susceptible population in Nizamuddin Basti was taken as 12,000 (it has a floating population of 2000-5000 per day and a population density approx 70,000 k m 2 .) (Source: Aga Khan Developmental Network). Dharavi has a population of about 1,000,000. With a population density of over 277,136 per k m 2 . As the incubation period of COVID-19 is about 5 days, the number of infected cases (I compartment) on 15th April was assumed to be the number of new cases 5 days thereafter, i.e. on 20th April. It is especially interesting that the E compartment of asymptomatic infectious individuals, who are accountable for increased case numbers even after improvement in healthcare facilities for symptomatic cases.

Analysis of hotspot districts

With the recent updates on COVID-19 cases in India, it has become evident that some parts of the country are more affected than others. For instance, Maharashtra, Tamil Nadu, Delhi, Gujarat, Telangana, Karnataka and Uttar Pradesh are on the top of the list of most affected states in India. At a more disaggregate level, few districts of these states are more badly affected. The common trend in COVID-19 infection is that mostly the urban regions have been more affected than their rural counterparts. This is primarily due to the fact that the virus was introduced by travellers from abroad. The variables used in this study are described in detail in Table  3 below. The summary measures of population density and number of COVID-19 hospitals reveal a large variation between the districts, the former ranging from 161 to 27730 persons per sq.km. whereas the latter ranges from no hospitals to 62 hospitals in a district.( https://covidindia.org , https://www.census2011.co.in/district.php ). These variations necessitate performing a cluster analysis to optimally classify the districts into homogeneous groups. Both hierarchical clustering and k-means clustering methods are done using the confirmed cases, population density and number of COVID-19 hospitals.

If we focus on the distribution of COVID-19 cases across states, the metropolitan areas are most affected in most of them. For Maharashtra (Fig.  3 ), the district marked in red is in the extreme west coastal region, Mumbai city and Mumbai Suburban. Similarly, in Gujarat, the most affected district is Ahmedabad. In Uttar Pradesh and Tamil Nadu the main hotspot districts are Agra and Chennai respectively. In West Bengal the main focus is on three districts, i.e. Kolkata, Howrah and North 24 Parganas (as shown by the red zones in the state map).

Distribution of COVID-19 cases in districts of selected states. These maps were created by the authors using R, packages ggmap , maps , maptools and rgeos

The population density map shows a similar pattern across districts in the states, refer Fig.  4 . However, the distribution of number of COVID-19 hospitals in the districts vary from the distribution of confirmed COVID-19 cases, refer Fig.  5 . The distribution of hospitals is much less skewed than the population density and COVID-19 cases. The state government and the central has been proactively trying to augment hospital facilities. The almost even distribution of the COVID-19 special hospitals in districts is a result of those initiatives.

District-wise population density in selected states. These maps were created by the authors using R, packages ggmap , maps , maptools and rgeos

Distribution of COVID-19 hospitals in districts of selected states. These maps were created by the authors using R, packages ggmap , maps , maptools and rgeos

The k-means clustering leads to three homogeneous clusters. The first of these, which include Mumbai, Thane and Chennai, have large number of confirmed COVID19 cases, number of hospitals as well as high population density. The second cluster has the lowest number of confirmed cases, COVID-19 hospitals and population density among the three. It includes cities like Kolkata, West Delhi, Central Delhi and Hyderabad. Unlike the first two clusters, cluster 3 has very high population density, but low number of COVID-19 hospitals and confirmed COVID-19 cases. Table  4 presents the centroids of each cluster, the central tendency values for each variable in each cluster. A diagrammatic representation is shown in Fig.  6 .

Visualization of three clusters generated from k-means clustering

Thus it is observed that the burden on the healthcare system is maximum in the cluster one districts, while with their high density but low confirmed cases cluster three comprises of districts which have been effective in controlling the disease.

The hierarchical clustering (as shown in the dendrogram below in Fig.  7 ), done to show the closeness of two districts in terms of the above three parameters, corroborates our findings from the k-means clustering.

The dendrogram above is a result of hierarchical clustering. Based on the number of active positive cases of COVID-19, fifty worst hit districts of India were used to create clusters of homogeneous districts. Six clusters were created, demarcated in the diagram with the boxes around clusters of districts that had similar number of active positive COVID-19 cases

Nizamuddin : a case study

In the wake of the global crisis due to COVID-19, almost all affected countries, have seen an exponential growth in the number of confirmed case count. In most of such countries, doctors discovered a gaggle of individuals who got infected at one place and mostly at around the same time. Such groups are termed clusters . In India one such case has been identified at the Nizamuddin basti in Delhi with its highly dense population. There have been numerous studies over the years, [ 17 ], that describes this area of Delhi as one of the more populous with almost 1500 households and a large floating population as well. Another study [ 18 ] discusses the development of the highly populated Nizamuddin basti around Hazrat Nizamuddin Auliya mosque. It is this locality that has been identified as a massive cluster , after a religious congregation held in mid-March led to COVID-19 spread among the attendees; at least 130 cases have been identified as having originated from this cluster.

As a special case study, we’ve used the SEIQHRF model to simulate the spread of the virus through infected individuals from the Nizamuddin cluster . A stochastic individual contact model (ICM) is employed to simulate the baseline projections of the timeline of establishment period of the virus, illness duration and survival time of the case fatalities. In the five panels of Fig.  8 we provide general statistical properties of the model in relation to some of the major parameters estimated.

The incubation period for the virus has a median of about 10 days and in few cases could reach 20 days or more.

Nizamuddin tablighi cohort duration frequency distribution

For some, isolation started much later, could be as long as 10 days. However, many people took less than a couple of days before they started to isolate themselves because of some COVID-19 like symptoms.

Illness duration seems span around 25 days. Although this may vary based on the individuals comorbid conditions.

Hospital care duration is about 10 to 15 days mostly, which seems to match actual observations.

Survival time of case fatalities is seen to be mostly between two to five days. However few fatal cases have had a survival time as high as 20 days.

The next plot shows the prevalence of COVID-19 among the Nizamuddin population in the various compartments of the SEIQHRF model. Prevalence is the number of people in each compartment at each point in time (each day). Given the dense nature of the population in the Nizamuddin basti the act parameter (average number of exposure events between infectious individuals and susceptible individuals) for this area is quite high making it a hotbed for the virus spread within the community. From Fig.  9 we can see how peaked the distribution of exposed individuals is in the neighbourhood. Also due to the delayed signs of this viral infection among individuals, the ones living in these densely populated areas unknowingly pass on the virus to others. The prevalence of cases where self-isolation by individuals is undertaken has a comparatively low peak, thus isolating oneself when symptoms are visible reduce the risk for others. The model estimate that from 15,000 susceptible people, almost as many as 15000 other people are potentially exposed during the 3 days event, and from this almost 15000, about 4000 are potentially infected and require hospitalization. However, due to the delayed signs of the infection causes possible delays in the people getting to the hospital for checking, which causes about 20 days delay in the infected person to be hospitalized. Furthermore, the number of people who went to self-isolation is predicted to be very small.

Prevalence simulation of COVID-19

In Fig.  10 we have isolated the infected, hospitalized and fatal compartments to look at them more closely. It shows that of the almost more than 4000 required hospitalisation, although with a delay of almost 15 days. The model used here predicts the prevalence in each compartment; shows the number of individuals in each compartment who have the condition during the time period under study. As we have stated before, sometimes the delayed signs of the infection cause a lag between the time infection is identified and it is serious enough to require hospitalisation. The time period of this study (second phase of lockdown in India) thus includes individuals for whom the infection was identified earlier than the present time but it aggravated enough to require hospitalisation much later. Thus making the predicted number larger than the size of the infected cohort. There were more than 2500 people infected. However, the number of fatal cases remained low, around 150.

Prevalence simulation COVID-19 confirmed cases

Apart from the baseline scenario, we have tried to follow the path of prevalence in each compartment after the implementation of several interventions.

Some of these administrative interventions are implemented in the simulation of the Nizamuddin cluster and represented in Fig.  11 a. A side-by-side comparison of similar implementation in Delhi is in Fig.  11 b. A list of interventions considered in this study is;

I 1 : Increase quarantining of symptomatic individuals during the first 15 days after the beginning of second phase of lockdown, i.e. 15th April 2020.

Comparison of prevalence between Nizamuddin cohort and Delhi

I 2 : Starting Social Distancing from 15th April, 2020: The Government of India announced a second phase of lockdown from 15th April, 2020 till 9th May, 2020 as an intervention to stop the COVID-19 pandemic. Hence, as an image of Social Distancing, we gradually reduced the frequency of exposure events from 20 to 10.

I 3 : Having introduced further social distancing from 15th April, 2020, we also introduced another intervention by increasing the hospital bed capacity to triple the initial number considered during the first 15 days of lockdown phase 2.

I 4 : Imposing more severe social distancing by the end of second phase of lockdown, thereby further reducing the number of exposure events from 10 to 5.

I 5 : Imposing a combination of increased social distancing and quarantining during the first 15 days of lockdown 2.

We will thereafter refer to the interventions as I i for i =1,2,3,4,5.

The same set of interventions have been used for other states as well, where attendees from Nizamuddin have returned after the convention.

The baseline plot shows that the number of infected/infectious individuals in the Nizamuddin cluster is close to 10,000, almost double of that in Delhi. This can mostly be s result of the very fact that in an exceedingly dense geographic area there is a larger propensity for people to mingle with more number of individuals, thus increasing the prevalnce of the spread of COVID-19 manifold.

In both Nizamuddin cluster and Delhi, the curve of infected/infectious individuals have flattened with implementation of the above interventions. Among the administrative mediation, increasing social distancing as well as self-isolation starting on day 15, after the beginning of the disease outburst, produces the best results in flattening the disease spread curve. However, a comparison shows that the Nizamuddin cluster increased the number of confirmed cases and also the number requiring hospitalisation. The numbers have almost doubled from that of Delhi.

The Nizamuddin cluster was discovered in mid-March and at least 130 cases in India have been identified as having originated from this cluster. There was then a second wave of infectious individuals identified in several other states that were linked to the Nizamuddin cluster. Tamil Nadu, Delhi, Telengana, Gujarat, Maharashtra are a some of such states.

In each of the states above (Tamil Nadu, Delhi, Telengana) the number of infected individuals linked to Nizamuddin cluster are 72, 24 and 6 respectively. The comparison in sets of Figs.  12 , 13 , and 14 show that in the second phase the number of infected individuals had seen a multitudinous increase. Thereby, increasing the danger of disease spread within the respective states. However, the simulations reveal that the administrative interventions, if implemented strictly, flatten the curve of disease spread. Specifically, the best results are seen if social distancing is strictly practised by day 15 since the disease outbreak.

Comparison of baseline prevalence with those with interventions for Delhi

Comparison of baseline prevalence with those with interventions for Tamil Nadu

Comparison of baseline prevalence with those with interventions for Telangana

In Table  5 first panel, the maximum predicted numbers in each compartment is presented for Nizamuddin basti right after the Tablighi Jamaat congregation (from SEIQHRF model fitting, also shown in Fig.  11 a). With the choice of parameters defined in “ Choice of parameters ” section, the baseline model is fitted, but I 1 through I 5 are the various interventions applied and the corresponding parameters have been modified accordingly. For instance, in the baseline case the projected number of infected cases was maximum around 5000, with the implementation of I 1 i.e. increased advocacy of quarantining the maximum projected individuals infected increased even more (around 7500). However, for I 2 through I 5 this number decreased. Thus, social distancing, augmentation of hospital capacity and severe quarantining implemented together prove to be useful in flattening the curve. The second and the third panel are predictions from Delhi during the first phase of lockdown, starting point is 24th March 2020. The third panel shows the predicted number of individuals in each compartment after the attendees of Nizamuddin meeting dispersed. A comparison of infected individuals increased manifold between the first lockdown and after Nizamuddin congregation. The number of infected before Tablighi Jammat (1000) was the lowest for intervention I 5 i.e. increased social distancing and quarantining. However, for the Nizamuddin cohort a severe imposition of social distancing worked better than any other intervention.

Similar to Table  5 we have also tabulated the maximum predicted number of individuals in each of compartment for Tamil Nadu and Telengana for both before and after phases of the Nizamuddin meeting, results shown in Tables  6 and 7 . In case of Tamil Nadu (Table  6 ), a combination of social distancing and quarantining during the first lockdown phase visibly lowers the number infected (1000), however, after the attendees from Nizamuddin cohort returned to the various states, Tamil Nadu and Telengana were one of the largest clusters of COVID-19 infections. In the second phase therefore increasing the hospital capacity helped in flattening the curve of infected persons. With the augmented hospital capacity proves to be an effective intervention. For Telengana (Table  7 ), in both phases 1 and 2, i.e. before and after the Nizamuddin cohort returned to the state, augmented hospital capacity seems like the best among the five interventions. Commensurate with the number of susceptible people in the states we also look at the number of fatalities and the ones requiring hospitalisation. But, the records of the number of days of hospitalization are not publicly available by each patient, thus making if unreliable to infer much from the simulation on how long the period would be. We also cannot claim the fatality rates with too much certainty because there are many unknown parameters involved and not much data is available for the co-morbidity conditions of the patients either.

Dharavi: a case study

Dharavi is considered Asia’s largest slum, one of the most densely populated areas in the world (with 2.6 lakh people per sq. km) and, now, also marked as a containment zone for COVID-19. When driving through the lanes and by-lanes of Dharavi, thronging with people, it is evident that the one necessary norm to prevent the spread of COVID-19, social distancing, is practically impossible to implement here. Most of the houses in the area are merely 10X10 feet, it is a challenge to keep people confined to such small area. In addition, there is a major problem with the common toilets that inhabitants have to use, which makes the containment of the virus an impossible task. Although the first case of COVID-19 in India was reported on the 30th of January, by middle of March only mere eight positive cases of COVID-19 were identified in Mumbai. All of these cases had a travel history abroad. However, by the end of March, the number of positive cases started growing exponentially. In Dharavi the first case was observed on the first of April, [ 19 ]. The index patient was a 56 year-old garment shop owner complained of high fever and cough. When his symptoms worsened he was admitted to the hospital where he succumbed to the disease before civic officials could talk to him about the people he might have come in contact with. So the officials began their contact-tracing exercise. It came to light during their investigation that a few days before the garment shop owner started showing the signs of COVID-19 infection, he had hosted a party with some people in his house, and all these guests were attendees of the religious event that took place in mid-March in Nizamuddin area of Delhi.

Other than the constraints of space that make the inhabitants more exposed to the virus, most COVID-19 positive cases in Dharavi have been found to be asymptomatic. The silent carriers living in the area were unaware of how many people they were infecting with the virus. A simulation run using the SEIQHRF model produces the number of infected/ asymptomatic, infectious, self-isolated, hospitalised and fatal cases in Dharavi (Fig.  15 ).

Prevalence plots in various compartments for Dharavi

Due to the very high act parameter value for Dharavi the SEIQHRF model algorithm is used to simulate the scenario for a period of 30 days. The results illustrate the predicted course of COVID-19 and the impacts of various experimental interventions. Dharavi has especially been a challenge for the Maharashtra government, slowly turning out to be the COVID-19 capital of the state. From Fig.  15 it is seen that the prevalence rate among the residents was very high since the beginning of the outbreak. The lack of space to practice social distancing and the use of common toilets, exposed them more to the risk of acquiring the virus. In the baseline model the number of infectious/asuymptomatic patients is as high as almost 10,000. However, measures like increased social distancing and self-quarantine is expected to help in flattening the curve to some extent. From the panel on the left we can see that with increase in social distancing and quarantining the number of infected/asymptomatic has come down by almost 2500. There is also a delay in the prevalence number reaching it’s peak, which is almost 10 to 15 days. Also the panel on the right reveals predicted number of people requiring hospitalization. Dharavi alone predicts a requirement of 200 hospital beds for hospitalization. As, of 11th July 2020 the total number of positive cases are 2359. The fatality rate in Dharavi increased from about 3% to 4.1% over a period of two weeks between May 5th and May 20th.

Amidst all the challenges, Dharavi has emerged as one of the few successes in containing the spread of COVID-19 with 1952 recovered cases of its 2359 confirmed and only a handful 166 remaining active. According to the Brihanmumbai Municipal Corporation officials, the steps taken in Dharavi can be defined by four T’s- tracing, tracking, testing and treating. Almost as many as 47500 houses were screened, while 14970 people were screened in mobile vans. In the study done by [ 20 ], the author states that the WHO has also acknowledged Dharavi’s success in controlling the spread of COVID-19 and mentioned that it should be seen as an example across the world. The WHO has also commended the Dharavi model which is mostly based on community engagement, testing, tracing, isolating, and treating in order to break the chain of transmission. Quarantine facilities were ramped up with the use of schools, marriage halls and sports complexes. The strict implementation of lockdown and an endeavour to treat all COVID-19 patients in Dharavi itself proved successful in containing the infection. A proof that these interventions were fruitful is the fact that while in April, the doubling rate in Dharavi was 18 days, it gradually improved to 43 days in May and slowed down to 108 and 430 days in June and July respectively. In a recent press meet officials from WHO cited Dharavi as one of the few cases around the world where a massive breakout of the infection could still be brought under control, [ 21 ]. In [ 20 ] the author reports that he municipal administration of Mumbai very aptly called the Dharavi model as “chasing the virus” rather than waiting for people to report it. According to [ 22 ] under the able leadership of the Mumbai municipality, Brihanmumbai Municipal Corporation (BMC), private medical practitioners, social activists, community leaders, and non-governmental organisations were roped in to battle the pandemic on a war footing. Measures were taken to give older people special attention. Most symptomatic cases were treated at community centres, except when critical and time was invested in trust building efforts to gather public support for containment of the disease. In this study the authors report that unlike the traditional approach of passive screening as undertaken elsewhere in India, the civic authorities of Brihanmumbai Municipal corporation adopted a proactive screening strategy in Dharavi and went door-to-door looking for COVID-19 suspects. Which supports the claims made by the municipal administration of Mumbai, that they were chasing the virus [ 20 ].

The various scenarios analysed in this paper introduce the various interventions undertaken and compare the course of COVID-19 under each of them with a baseline model. To summarise the outcomes some important observations made are as follows: In the baseline model, the results are from our model fitting on a hypothetical population of 1000 people. The findings are summarized as follows;

Time taken for the COVID-19 pandemic to abate is about two months.

As the population density of Delhi is very high, many people are infected, although asymptomatic.

We see typical exponential behaviour, although these are prevalence numbers, not incidence. The prevalence counts tends to start with an exponential growth; however they then diminish.

The number of people who require hospital facilities is not too large.

Although the growth rate is much lower, the number of cases in the fatality compartment is increasing consistently.

But the various interventions have varied effect on the spread of COVID-19. In most of the states in India the best results are seen in implementation of early social distancing and quarantine of the infectious individuals. In states where a lot of attendees from Nizamuddin had returned, extra care was taken to isolate them and that to some extent alleviated the risk of infection spread. We see an exponential behaviour in the growth of prevalence in the panels, although these are not incidence. Prevalence starts with an exponential growth but then diminishes. That’s what we are seeing here, so that’s a good sign.

In case of Nizamuddin, the highest frequency is seen between day 5 and 7, the individuals for whom the disease turned fatal mostly survived for five to seven days. The attendees of the gathering in Nizamuddin then exposed some of the states/UTs, like, Delhi, Tamil Nadu and Telangana to a second wave of COVID-19 infection. A side-by-side comparison of prevalence in these states during the first and second wave show a significant growth in number of COVID-19 positive cases.

Dharavi is one of the unlikely success stories in India. In spite of being one of the largest slum areas of Asia, the Brihanmumbai Municipal Corporation has managed to control COVID-19 spread in an area merely 2.1 k m 2 housing almost a population of 10,00,000. The four T’s- tracing, tracking, testing and treating have worked wonders for the slum and when large metropolises around the world like New York, Los Angeles, London reeling under the COVID-19 attack, a slum in Mumbai India has managed to reduce the reproduction number to a record low. In comparison to Dharavi, Mumbai a city which Dharavi is a part of, has suffered more severe fate at the hands of the deadly virus with almost 2.61 lakh active cases as of 6th November 2020.

Limitations and future studies

The main limitation of this study is that the COVID-19 scenario is still very dynamic and evolving with each passing day. Predicting the true path that the virus is going to take, is difficult if the parameters in the model are incorrectly specified. And with the ever changing COVID-19 landscape, it is one of the major challenges in model fitting. With new strains of the virus emerging, the unavailability of disaggregate level data of the confirmed cases makes modelling a challenge. There is also lack of available data on co-morbid conditions of the patients hospitalised with COVID-19 along with lack of data on actual hospitalisation. This adds to the already existing impediments in the way of modelling the path of the virus spread. Availability of such data would enable a more robust forecast of the number of confirmed COVID-19 cases. The data would also help in early detection of COVID-19 clusters which would be helpful for executing public health policies. Also the government has been trying to implement new policies to curb the spread. During the time-line of this study we have only looked at the first three months of the pandemic outbreak in India. As an extension of this study we would like to tap into the increasing data on COVID-19 and follow the path of the virus in the more recent unlock phases in India. Also a longitudinal analysis of COVID-19 data from the worst hit districts and following them over the months can give us more useful insights.

The top 50 districts of India where most severe outbreak of COVID-19 was seen are analysed through cluster analysis using their respective population densities and number of COVID-19 hospitals. The results helped identify the cities where similar pattern of disease outbreak was seen as well as the ones with homogeneity in terms of COVID-19 hospitals and population density. An in depth look at the various mediations undertaken to curb the growth of COVID-19 in India have shown different levels of improvement in flattening the disease curve. As the attendees of Nizamuddin congregation returned to their home states, like Tamil Nadu, Telangana, and other parts of Delhi the second wave of surge in confirmed case numbers is seen. A comparison between the first and second wave of COVID-19 infection in select states also support this finding. A further study of Dharavi, one of the success stories of the biggest slums in Asia, is undertaken and the strategy of 4 T’s-tracing, tracking, testing and treating has shown to reduce the spread of the disease dramatically.

Availability of data and materials

The datasets used and/or analysed during the current study are simulated based on the epidemiology model used in this study. The data used and prevalence plots generated thereafter have been simulated and created using the R programming language, packages EpiModel , and incidence .

Abbreviations

Corona Virus Disease 2019

Susceptible-Infectious-Recovered model

Susceptible-Exposed-Infectious-Recovered model

Susceptible-Exposed-Infectious-Quarantine-Hospitalised-Recovered-Fatal model

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Acknowledgments

Not applicable.

This study is under the “LISA 2020- Building Data Science Capacity” project funded by USAID and University of Colorado Boulder. The funder of the study sponsor had no role whatsoever in study design; in the collection, analysis, and interpretation of data; in the writing of the report.

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International Management Institute, Kolkata, India

Pooja Sengupta

Department of Statistics, University of Calcutta, Kolkata, India

Bhaswati Ganguli, Sugata SenRoy & Aditya Chatterjee

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BG, SSR, AC, and PSG co-conceived the study. BG and PSG led the data analysis. PSG wrote the first draft of the manuscript, and all the authors provided crucial input on several iterations of the manuscript. SSR, BG, and AC provided useful insights into designing the simulation study. All authors read and approved the final version of the manuscript.

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Correspondence to Pooja Sengupta .

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Sengupta, P., Ganguli, B., SenRoy, S. et al. An analysis of COVID-19 clusters in India. BMC Public Health 21 , 631 (2021). https://doi.org/10.1186/s12889-021-10491-8

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Received : 01 September 2020

Accepted : 23 February 2021

Published : 31 March 2021

DOI : https://doi.org/10.1186/s12889-021-10491-8

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