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Rapid risk assessment of acute public health events

This manual has been developed to guide rapid risk assessment of acute public health risks from any type of hazard in response to requests from Member States of the World Health Organization (WHO). The manual is aimed primarily at national departments with health-protection responsibilities, National Focal Points (NFPs) for the International Heath Regulations (IHR) and WHO staff. It should also be useful to others who join multidisciplinary risk assessment teams, such as clinicians, field epidemiologists, veterinarians, chemists, food-safety specialists.

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rapid_risk_assessment.md

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We all regularly use a risk based methodology when making decisions in day to day life, without thinking about it. The Rapid Risk Assessment or Rapid Risk Analysis (RRA) methodology helps formalize this type of decision making and ensures that the process is reproducible, consistent and the results are easy to communicate.

See also Assessing Security Risk for an introduction to risk and our processes related to risk.

Rapid Risk Assessment

A typical Rapid Risk Analysis/Assessment (RRA) takes about 30 minutes. It is not a security review, a full threat-model, a vulnerability assessment, or an audit. These types of activities may however follow an RRA if deemed appropriate or necessary.

The main objective of the RRA is to understand the value and impact of a service to the reputation, finances, productivity of the project or business. It is based on the data processed, stored or simply accessible by services.

Note that the RRA does not focus on enumerating and analyzing security controls. The RRA process is intended for analyzing and assessing services, not processes or individual controls.

Data is the most important item in risk management. Software, websites, infrastructure, networks and people handle, process, exchange and store data.

The RRA focuses on creating a summary of the risks associated with your data. Key points:

• Quick! The RRA takes 30 to 60 minutes maximum.
• Very high-level . Details are for complete threat models. The RRA can become a complete threat model over time though!
• Concise, readable . Short and with clear risk levels.
• Easy to update . Can be run during any phase of the project development and continuously updated.
• Informative . Collects risk impact and a data dictionary. Also collections information about how the service functions.
• Let you know what to do . The RRA includes the list of recommendations from the security team with a priority for each item.

This helps to make the following type of risk-based decisions:

• Is the security provided by a given platform appropriate to host a specific classification of data?
• How much should we care about maintenance, etc?
• Is there anything obvious we should really look at fixing right now?
• Where should we focus our efforts to significantly increase the security or the service?
• Did we forgot anything, or had any blind spot we hadn't though of?

Sample use scenario

Firefox accounts store user data:

• What happens if that data is disclosed to the world? What happens if the service goes down? What happens if the data is modified by someone unauthorized?
• Do these events put us at financial risk? Is our productivity affected? Is our reputation affected?
• What types of data does Firefox accounts handle? How sensitive is the primary type of data used by Firefox accounts?

The RRA risk table facilitates discovering the answers to these questions.

How-to: Attending and running RRAs

When to run rras what do i need to bring or do.

RRAs are designed to be created and updated as needed, at any time, with or without an associated meeting. That being said, you should run the first RRA during the design or architecture phase of new services together with a trained risk analyst.

It is also recommended to have these things available for the RRA creation:

• Name of a person or/and team responsible for the service.
• Data flow diagram.
• List the kind of data that will be processed or stored: secrets, credentials, public data, confidential data, and anything else that may be important for this service.
• An understanding of how the service works.

When NOT to run an RRA?

RRAs can only be run on services. If you have a question about a specific feature or design choice, and how it's going to impact other services: find which service your feature is tied to and see if there is already an RRA available. Otherwise, request an RRA for that service, this will help us (and you) assess your feature or design choice!

Note on large services Large services may be be split into multiple smaller services or sub-services that handle a specific type of data and expose a limited set of features. This choice has to be made when running the RRA. If the sub-services are owned by different teams, it is a strong indicator that multiple RRAs should be run.

Large services that cannot be split up not only lead to a complex assessment, but also may indicate that the service itself needs to be re-designed in a more secure fashion.

What to focus on during an RRA?

• Getting value for the service owner . The service owner needs to understand what is most important to protect and if they have any blind spot.
• Data . Fill in the data dictionary. You need to know most of the data the service will have access to, stored or processed.
• Impact assessment . The RRA is the authority for impact levels and these are paramount. How bad can things get, what's the worse case scenario?
• Recording threat scenarios . What attack scenarios were considered? Would someone else understand it?

What to NOT focus on:

• Gathering security controls and figuring out how effective they are. Don't do that! This information may be recorded if it comes up but do not focus on it as this is very time consuming. If the service is considered risky, specific processes can be recommended in the RRA recommendations section to assess specific controls separately.
• Likelihood, Security provided by service. Don't spent much time there! It is very hard to assess likelihood in most scenarios, and easy to get lost in "what if". We have specific, separate processes to assess likelihood. Some quick questions may still help though, such as "what's the security history of this service?"

Guided process for risk analysts: Running your RRA in ~30 minutes

This is a guided example of how to run an initial RRA. You will:

• Invite the relevant people to a meeting.
• Help them figure out risk impacts and record everything in the RRA doc.
• Help them figure out the next steps.
• Make them feel like they own the RRA document (and they do!).

Before the initial RRA meeting

Mozilla staff should follow these instructions (restricted) to request an RRA.

For other interested folks, here's a summary of the steps:

• Ensure no previous RRA exist; if it does, just enhance the current RRA document
• Everyone can preview a copy of the RRA Template (or copy if you have a Google account).
• Invite 1 or 2 members (product/service owners, lead engineers, etc.) related to the service with a bit of technical knowledge.
• Ensure the invitees attempt to bring a data flow diagram and have an understanding of the data the service stores or processes.
• You do not want more than 4 or 5 people total as this will slow down the RRA significantly. Most RRAs are run 1 on 1 (2 people total).
• Make sure everyone invited has edit rights to the document, and have the document opened in front of them when the RRA starts (you can also just share your screen).
• If this is anyone's first RRA, ensure they understand the goals of the RRA and give them a short introduction to what the different steps will be. This both help them follow, and show that you have control of the meeting (see next section on time management).

Initial RRA meeting

Time management - take control.

You will be responsible for the time management when running the first RRA for a service. This means, you will sometimes have to cut a discussion short and be assertive: we have a tendency to jump directly to discussing security controls during risk discussions. While valuable, this is not the initial purpose of the RRA, and controls can be better discussed once the impacts have been clearly defined.

Example red flags that indicate you should re-focus the current discussion:

• The discussion languishes (>1 minute) around "how to mitigate this very issue" or "we're doing X to ensure this never happens".
• The discussion focuses on process preferences, changes, etc. instead of just filing the RRA document.
• The discussion about how the service works takes too long (>15 minutes) and the owner has to lookup every single detail (you only need an overview at this stage, or the owner has to come back when they know what service they want to look at).

A good tip is to reserve 60 minutes of RRA time in the calendar, and plan to run the RRA for only 30 minutes . This leaves you with some room for error, and handle services that weren't well understood by their owners. Best case scenario, everyone will be happy when you cut the meeting short after only 30 minutes. In any case, always watch the clock! Having to use more than 60 minutes for the initial RRA is considered a failure and should not happen.

Running the RRA meeting

Note : If this is your first RRA, ensure that someone who has run RRAs previously is present to help you. It is good to have attended multiple RRAs before starting your own. Your experience and understanding is key to running a successful RRA that will help the teams and keep the service safe.

RRA Utilities : There is a menu at the top of the document called the "RRA Utilities" menu. Use it to set risk impact, levels, data classification and marking the RRA as reviewed. Do use it as our scripts rely on this to copy RRAs to our RRA API. Note that these utilities are only available in the internal Mozilla tools.

Metadata (1min)

• Fill in the service name
• Fill in the service owner: ask whom would be taking the decision to turn the service off in case of an incident, if it's unclear. That is the service owner.
• Lookup the owner's closest director or VP and add this as well.
• Leave "service data classification" and "highest risk impact" empty for now.

Service Notes (5min)

This is where you put any notes that you feel are relevant to the understanding of the service, security, etc. Ask the service owner what the service does and a little bit of how it works. Ensure that you understand the service well. You should be able to reformulate what the service does, and the service owner to agree on your formulation. Generally, you want to copy a diagram of some sort and have links back to the RRA request bug, and the service's own website (which may be a vendor). This is also a good time to check whether the team requested a vendor assessment using a questionnaire for example.

Feel free to go back to this section at any time to add any further notes.

Data Dictionary (5-10 minutes)

We want to know about all data the service will process or store (and not just store). Any data the service can touch or see is to be considered. You will need to ask the team or service owner about what kind of data the service processes or stores. Here are some examples:

• Specific configuration data
• User or service credentials, secrets

Set the data classification for each data type in the dictionary, such as "PUBLIC", "STAFF CONFIDENTIAL", etc. by using the "RRA Utilities" menu. Mozilla uses standard classification levels .

When you figure out what the bulk or the most important data is, set this as the "Service Data Classification" in the RRA metadata.

Threat Scenarios (5-10 minutes)

This is where we discuss potential attack scenarios and figure out how bad things could go (worse-case scenario). The RRA document itself contains tips about this section as well. We do not record the threat types, attacker types, etc. in this model in order to save time. Think about the easiest attack vectors ("threat scenarios"). While we focus on recording impact, you should also ask if anything already happened and make a note if so, as this indicates a possible higher likelihood for the impact to occur.

Record all results and make sure that you set an impact level (use the "RRA Utilities" menu for this)

Confidentiality : What happens if all the data is disclosed to the world?

Integrity : What happens if the data is incorrect, misleading, website defaced, etc.?

Availability : What happens if the data or service is missing, deleted, or currently unreachable?

For each, run through these questions and assign an impact level if appropriate:

• Do we get in mainstream news? ( MAXIMUM impact )
• Do we get in the technical news? ( HIGH impact )
• Do we receive emails, bugs, twitter messages, etc? ( MEDIUM impact )
• Not much? ( LOW impact )
• Less than 24h? ( LOW impact )
• Less than 2 days? ( MEDIUM impact )
• Less than a week? ( HIGH impact )
• More? ( MAXIMUM impact )
• Less than 2h? ( LOW impact )
• Less than 24h? ( MEDIUM impact )
• Less than 2 days? ( HIGH impact )
• Would it cost money? How much?

Enhance these scenarios, eventually to create a complete threat model if the assessed impacts are HIGH or MAXIMUM and if further security work is required, or the RRA is revisited.

Additional tips

• Whenever the productivity impact is HIGH or MAXIMUM, there is probably also a financial impact due to the cost of the workforce being impacted.
• Financial risk is sometimes hard to define, in particular when tied to contracts. When in doubt, skip it.
• If you have any HIGH or MAXIMUM impacts, propose that a more complete threat model and pen-test be run.
• Educate the project owners and lead developers of the project about the meaning of these risks and how the RRA can help them make decisions such as which operational environment to select, what technologies to use, and how much effort to put into securing the project.

Recommendations (5 minutes)

While the RRA is not meant as a complete review, recommendations do come up and this is a great time to have a quick 5 minute chat about these.

• Ensure all recommendations that came up (from you or the team) are mentioned here. It's also ok to fill them as you go!
• Use the "RRA Utilities" menu to set an impact for the recommendation ("if followed, how much would it help the service?") as this allow service owners to prioritize work.
• Ensure logging and access control have been mentioned. Can these be improved? Should we alert on events?
• Does this service have an incident response plan defined?
• Is the service using SSO for login?
• Is there a web site that can be assessed? If so, you can file a vulnerability assessment request directly.
• Did the service follow the security checklist and principles ?

Wrapping up

• Make sure you've filled the "Service Data Classification" up top according to your data dictionary (what you consider to be either the bulk of the data or the most important data is your classification)
• Make sure you've also filled the "Highest Risk Impact", this is basically the high impact recorded in the threat scenarios.
• Present the current risk impact and ask if they think it's reasonable.
• Present the current recommendations and ask if they think anything's missing and what they should start with.
• Ask the team if there's any additional security related question they want to ask or if anything wasn't covered.
• Add Google docs comments to the recommendations and assign the comment to the service owner, to ensure they've also been notified by email.
• Make sure you have marked the RRA as reviewed by yourself in the "RRA Utilities" menu.

Reference documents and similar work

• https://binary.protect.io/workcard.pdf
• https://en.wikipedia.org/wiki/ISO_31000
• https://web.archive.org/web/20141118061526/http://www.riskmanagementinsight.com/media/docs/FAIR_introduction.pdf
• RRA Template
• RRA Support code for the Google Docs template
• RRA Parsing code (convert to spreadsheet/db) for the Google Docs template

Content Search

Operational tool on rapid risk assessment methodology, attachments.

1 Introduction to purpose and scope of document

Rapid risk assessments (RRA) are undertaken in the initial stages of an event or incident of potential public health concern while more comprehensive risk assessments, which often include the conduct of full systematic reviews, are produced at a later stage of an event, usually when more time and information are available. The operational goal for ECDC is that an RRA should be produced within a limited time frame. The aim of this document is to provide an operational tool to facilitate the structured and reproducible development of rapid risk assessments for communicable disease incidents. The target audience is primarily experts responsible for the rapid assessment of communicable disease threats at the European level, although the document may also serve as a useful reference for experts with similar responsibilities at a national or subnational level. The initial assessments of potential communicable disease threats can be complex and challenging as they must be produced within a short time period when information is often limited and circumstances can evolve rapidly. However, RRAs should still be based on the structured identification of key information from all readily available sources at the time in order to provide a clear estimate of the scale of the health threat while documenting the level of uncertainty.

A good RRA should be:

• consistent and transparent to ensure fairness and rationality

• easily understood by the intended target audience

• reproducible

• based on the best scientific evidence available at the time, well-documented and supported with references to scientific literature and other sources, including expert opinions

• regularly reviewed (may be done at preset intervals) and updated if needed when additional new information becomes available

• complemented by a log for decisions and actions based on available information; and

• contain a record of uncertainties (gaps in knowledge) and assumptions made in order to evaluate the effect of these on the final risk estimate and priorities for future research (dated and with version control).

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• Qualitative Assessments
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Related Topics:

• Center for Forecasting & Outbreak Analytics
• About Behind the Model
• Modeling and Forecasting
• Insight Net

How CFA Conducts Risk Assessments

At a glance.

Below, we explain the methods behind the Center for Forecasting and Outbreak Analytics' rapid risk assessments. Subject-matter experts make qualitative judgments based on available evidence in an evolving situation. For each population examined, we estimate overall risk by combining infection likelihood and impact.

Our assessments are qualitative. This means subject-matter experts make judgments based on available evidence, including epidemiological and clinical data, modeling analyses, and data on past outbreaks. We also consider information from international partners and media reporting.

For each population examined, we estimate overall risk by combining the likelihood of infection and the impact of the disease. The likelihood of infection refers to the probability that individuals in the population acquire infection, which depends on the likelihood of exposure, infectiousness of the pathogen, and susceptibility of the population (see definitions). The impact of infection considers factors such as the severity of disease, level of population immunity, availability and effectiveness of treatments, and necessary public health response resources (see definitions). We also assign confidence levels to each of our assessments, based on the quality and amount of evidence that underpins the assessment, as well as how well different lines of evidence corroborate one another (see definitions).

Our risk assessment methods are adapted from those developed by the World Health Organization and the European Centre for Disease Prevention and Control .

We are collaborating across CDC and with external partners to standardize and improve these methods, which will help facilitate comparisons across disease areas. We are also working with international partners to align on methods, address common challenges, and ensure we can more easily compare risk assessments across countries.

Definitions

Likelihood of infection definitions.

• Extremely low: An extremely small number of people are likely to be exposed, the pathogen is not very infectious, or the population is highly immune. We expect an extremely low prevalence of infection in the population, far less than 1% of the population.
• Very low: A very small number of people are likely to be exposed, the pathogen is not very infectious, or the population is highly immune. We expect a very low prevalence of infection in the population.
• Low: There are limited opportunities for exposure for most of the population, but exposure may be high in some areas or subgroups. The pathogen has at least moderate infectiousness or significant gaps in population immunity. We expect a low prevalence of infection in the population, potentially with pockets of higher prevalence.
• Moderate: Many people are likely to be exposed, the pathogen has moderate to high infectiousness, or the population has low levels of immunity. We expect a moderate prevalence of infection in the population.
• High: Most people are likely to be exposed, the pathogen has high infectiousness, or the population has very low immunity. We expect a high prevalence of infection in the population, with most of the population affected.
• Very high: The vast majority of the population is likely to be exposed, the pathogen has very high infectiousness, or the population has extremely low immunity. We expect a very high prevalence of infection in the population, with the vast majority of the population affected.

Impact of infection definitions

• Very low: The pathogen is very unlikely to cause severe disease for this population, there is a very high proportion of population immunity protecting against severe disease, and/or effective treatments are widely available. The disease is very unlikely to cause disruption to normal activities or require additional resources for public health measures.
• Low: The pathogen is unlikely to cause severe disease for this population, there is a very high proportion of population immunity protecting against severe disease, and/or effective treatments are widely available. The disease is unlikely to cause disruption to normal activities or require additional resources for public health measures.
• Moderate: The pathogen causes severe disease for a substantial proportion of this population or pockets within this population, there is limited population immunity protecting people from severe disease, and/or effective treatments are not widely available or accessible. The disease may cause significant disruption to the population and require significant public health resources.
• High: The pathogen typically causes severe disease for this population, there is a very low proportion of population immunity protecting against severe disease, and/or effective treatments are very limited or difficult to access. The disease could cause extensive disruption to normal activities and will potentially require a large amount of public health resources.
• Very high: The pathogen typically causes very severe disease for this population, there is a very low proportion of population immunity protecting against severe disease, and/or there are no effective treatments. The disease could cause prolonged and extensive disruption to normal activities and will potentially require a very high level of public health resources.

Confidence level definitions

We assess confidence based on the extent and quality of information used to make assessments, as well as how well different lines of evidence corroborate one another. We consider peer-reviewed published studies and confirmed reports from reliable government sources as high-quality evidence. Individual case reports and news articles are of lower quality.

• Low confidence: Assessment is based on information that is fragmented, poorly corroborated or upon data sources for which there are significant concerns or problems. There may be several information gaps that require numerous assumptions in order to draw conclusions for the assessment.
• Moderate confidence: Assessment is based on credibly sourced and plausible information, but the information is not of sufficient quality or corroboration to warrant a high level of confidence. The assessment acknowledges some information gaps or assumptions that underlie analysis.
• High confidence: Assessment is based on high-quality information from multiple sources, although such judgments are not a certainty. There are few information gaps and few assumptions are required to draw analytic conclusions.

Publication detail

Operational guidance on rapid risk assessment methodology

Publication metadata, available languages and formats, english (en).

• Publication details
• Related publications
• Published: 2011
• Corporate author(s): European Centre for Disease Prevention and Control ( EU body or agency )
• Themes: Public health
• Subject: disease prevention , epidemic , infectious disease , public health , respiratory disease

• Released on EU Publications: 2011-10-06

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COVID-19 response: effectiveness of weekly rapid risk assessments, Italy

Lutte contre la covid-19: efficacité des évaluations hebdomadaires rapides des risques en italie, respuesta a la covid-19: eficacia de las valoraciones semanales rápidas de los riesgos en italia, الاستجابة لكوفيد 19: فعالية التقييمات الأسبوعية السريعة للمخاطر، إيطاليا, 意大利新型冠状病毒肺炎应对措施：提高每周快速风险评估的效力, Меры реагирования на covid-19: эффективность еженедельных оперативных оценок рисков, Италия, flavia riccardo.

a Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.

Giorgio Guzzetta

b Fondazione Bruno Kessler, Trento, Italy.

Alberto Mateo Urdiales

Martina del manso, xanthi d andrianou, antonino bella, patrizio pezzotti, simona carbone.

c Ministry of Health, Rome, Italy.

Tiziana De Vito

Francesco maraglino, vittorio demicheli.

d Agenzia di Tutela della Salute (ATS) Milano, Milan, Italy.

Claudio Dario

e Regione Umbria, Perugia, Italy.

Enrico Coscioni

f Regione Campania, Naples, Italy.

Giovanni Rezza

Andrea urbani, stefano merler, silvio brusaferro.

After Italy’s first national restriction measures in 2020, a robust approach was needed to monitor the emerging epidemic of coronavirus disease 2019 (COVID-19) at subnational level and provide data to inform the strengthening or easing of epidemic control measures.

We adapted the European Centre for Disease Prevention and Control rapid risk assessment tool by including quantitative and qualitative indicators from existing national surveillance systems. We defined COVID-19 risk as a combination of the probability of uncontrolled transmission of severe acute respiratory syndrome coronavirus 2 and of an unsustainable impact of COVID-19 cases on hospital services, adjusted in relation to the health system’s resilience. The monitoring system was implemented with no additional cost in May 2020.

Local setting

The infectious diseases surveillance system in Italy uses consistent data collection methods across the country’s decentralized regions and autonomous provinces.

Relevant changes

Weekly risk assessments using this approach were sustainable in monitoring the epidemic at regional level from 4 May 2020 to 24 September 2021. The tool provided reliable assessments of when and where a rapid increase in demand for health-care services would occur if control or mitigation measures were not increased in the following 3 weeks.

Lessons learnt

Although the system worked well, framing the risk assessment tool in a legal decree hampered its flexibility, as indicators could not be changed without changing the law. The relative complexity of the tool, the impossibility of real-time validation and its use for the definition of restrictions posed communication challenges.

Résumé

Problème.

Après avoir pris ses premières mesures de restriction nationales en 2020, l'Italie avait besoin d'une approche solide pour surveiller l'épidémie naissante de maladie à coronavirus 2019 (COVID-19) au niveau régional, et fournir les données permettant de renforcer ou d'alléger les mesures destinées à l'endiguer.

Nous avons adapté l'outil d'évaluation rapide des risques du Centre européen de prévention et de contrôle des maladies en y intégrant des indicateurs quantitatifs et qualitatifs issus des systèmes de surveillance nationaux existants. Pour définir le risque lié à la COVID-19, nous avons associé la probabilité d'une transmission incontrôlée du coronavirus 2 du syndrome respiratoire aigu sévère, à l'impact immédiat des cas de COVID-19 sur les services hospitaliers, en procédant à des ajustements selon la résilience du système de soins de santé. Le dispositif de surveillance a été mis en œuvre en mai 2020 sans entraîner de coûts supplémentaires.

Environnement local

En Italie, le système de surveillance des maladies infectieuses repose sur des méthodes uniformes de collecte de données dans les provinces autonomes et régions décentralisées à travers le pays.

Changements significatifs

Les évaluations des risques réalisées toutes les semaines avec cette approche ont permis de surveiller l'épidémie à l'échelle régionale du 4 mai 2020 au 24 septembre 2021. L'outil a identifié les dates et lieux susceptibles de connaître une augmentation rapide de la demande en services de soins de santé si aucune mesure supplémentaire de contrôle et de lutte n'était prise dans les trois semaines.

Leçons tirées

Bien que le système ait fonctionné, inscrire l'outil d'évaluation des risques dans un décret législatif a réduit sa flexibilité, car les indicateurs ne pouvaient être modifiés sans réformer la loi. La relative complexité de l'outil, l'impossibilité de procéder à une validation en temps réel et son usage pour imposer des restrictions ont posé des problèmes de communication.

Situación

Tras las primeras medidas nacionales de restricción en Italia en 2020, se necesitaba un enfoque sólido para supervisar la epidemia emergente de la coronavirosis de 2019 (COVID-19) a nivel subnacional y proporcionar datos que informaran sobre el refuerzo o la flexibilización de las medidas de contención de la epidemia.

Se adaptó la herramienta de valoración rápida de riesgos del Centro Europeo para la Prevención y el Control de las Enfermedades, al incluir indicadores cuantitativos y cualitativos de los sistemas nacionales de vigilancia existentes. Se definió el riesgo de la COVID-19 como una combinación de la probabilidad de transmisión descontrolada del coronavirus del síndrome respiratorio agudo grave de tipo 2 y de un efecto no sostenible de los casos de la COVID-19 en los servicios hospitalarios, y se ajustó en relación con la capacidad de recuperación del sistema sanitario. El sistema de supervisión se aplicó sin costes adicionales en mayo de 2020.

Marco regional

El sistema de vigilancia de las enfermedades infecciosas en Italia aplica métodos de recopilación de datos coherentes en todas las regiones y provincias autónomas descentralizadas del país.

Cambios importantes

Las valoraciones semanales de los riesgos mediante este enfoque fueron sostenibles en la supervisión de la epidemia a nivel regional entre el 4 de mayo de 2020 y el 24 de septiembre de 2021. La herramienta proporcionó valoraciones fiables de cuándo y dónde se produciría un rápido aumento de la demanda de servicios sanitarios si no se incrementaban las medidas de contención o mitigación en las tres semanas siguientes.

Lecciones aprendidas

Aunque el sistema funcionó bien, el hecho de enmarcar la herramienta de valoración de los riesgos en un decreto legal dificultó su flexibilidad, ya que los indicadores no se podían modificar sin cambiar la ley. La relativa complejidad de la herramienta, la imposibilidad de validación en tiempo real y su uso para la definición de las restricciones plantearon problemas de comunicación.

ملخص

المشكلة بعد إجراءات التقييد الوطنية الأولى بإيطاليا في عام 2020، كانت هناك حاجة إلى أسلوب فعّال لرصد الوباء المستجد لمرض فيروس كورونا 2019 (كوفيد 19) على المستوى دون الوطني، وتوفير البيانات لدراسة إمكانية تعزيز أو تخفيف تدابير مكافحة الوباء.

الأسلوب قمنا بتكييف أداة لتقييم المخاطر السريعة تابعة للمركز الأوروبي للوقاية من الأمراض ومكافحتها، وذلك من خلال تضمين مؤشرات كمية ونوعية من أنظمة المراقبة الوطنية الحالية. كذلك قمنا بتعريف مخاطر كوفيد 19 على أنها مزيج من احتمالية الانتقال غير المُقيد لمتلازمة الجهاز التنفسي الحادة الشديدة لفيروس كورونا 2، والتأثير غير المستدام لحالات كوفيد 19 على خدمات المستشفيات، مع تعديلها فيما يتعلق بمرونة النظام الصحي. تم تنفيذ نظام المراقبة دون تكلفة إضافية في مايو/أيار 2020.

المواقع المحلية يستخدم نظام مراقبة الأمراض المعدية في إيطاليا طرقًا متسقة لجمع البيانات عبر المناطق اللامركزية في البلد والمقاطعات المستقلة.

التغيّرات ذات الصلة كانت تقييمات المخاطر الأسبوعية باستخدام هذا الأسلوب مستدامة في رصد الوباء على المستوى الإقليمي، طوال الفترة من 4 مايو/أيار 2020 إلى 24 سبتمبر/أيلول 2021. قدمت الأداة تقييمات موثوقة حول توقيت ومكان حدوث زيادة سريعة في الطلب على خدمات الرعاية الصحية في حالة السيطرة، أو إذا لم يتم زيادة تدابير التخفيف في الأسابيع الثلاثة التالية.

الدروس المستفادة على الرغم من أن النظام قد عمل بشكل جيد، إلا أن صياغة أداة تقييم المخاطر في مرسوم قانوني أعاقت مرونته، حيث لا يمكن تغيير المؤشرات دون تغيير القانون. أدى التعقيد النسبي للأداة، واستحالة التحقق من فعاليتها في الوقت الفعلي، واستخدامها لتعريف القيود، إلى ظهور تحديات في التواصل.

摘要

问题 2020 年意大利首次实施全国性限制措施后，需要采取可靠方法以监测新型冠状病毒肺炎（新冠肺炎）疫情在地方层面的蔓延情况，并提供数据以表明是否需要加强或放松疫情控制措施。

方法 通过纳入现有国家监测系统的定量和定性指标，我们调整了欧洲疾病预防和控制中心的快速风险评估工具。我们将新型冠状病毒肺炎风险综合定义为严重急性呼吸系统综合症冠状病毒 2 不受控制传播的可能性以及新型冠状病毒肺炎病例对医院服务的非持续性影响，并根据卫生系统的顺应力进行了调整。2020 年 5 月，在没有产生额外成本的前提下实施了监测系统。

当地状况 意大利传染病监测系统在全国各个分散的地区和自治省统一使用相同的数据收集方法。

相关变化 在 2020 年 5 月 4 日至 2021 年 9 月 24 日期间，使用这种方法开展的每周风险评估在监测区域层面疫情情况方面具有可持续性。该工具能够可靠地评估，如果在接下来的 3 周内没有加强控制或缓解措施，何时何地医疗保健服务需求会迅速增加。

经验教训 尽管该系统运作良好，但将风险评估工具纳入法令范畴限制了其灵活性，因为若不更改法律，则无法变更指标。该工具的相对复杂性、实时验证的不可能性及其在法规限定方面的用途导致产生了沟通挑战。

Резюме

Проблема.

После первых национальных ограничительных мер в Италии в 2020 году потребовался активный подход для мониторинга зарождающейся эпидемии коронавирусной инфекции 2019 года (COVID-19) на субнациональном уровне и для предоставления данных для обоснования усиления или ослабления мер по борьбе с эпидемией.

Подход

Авторы адаптировали инструмент для оперативных оценок рисков Европейского центра по контролю и профилактике заболеваний, включив в него количественные и качественные показатели из существующих национальных систем эпиднадзора. Авторы определили риск COVID-19 как комбинацию вероятности неконтролируемой передачи тяжелого острого респираторного синдрома, вызванного коронавирусом-2, и разрушительного воздействия случаев COVID-19 на больничное обслуживание, которая скорректирована с учетом устойчивости системы здравоохранения. Система мониторинга была внедрена без каких-либо дополнительных затрат в мае 2020 года.

Местные условия

В системе эпиднадзора за инфекционными заболеваниями в Италии используются последовательные методы сбора данных по децентрализованным регионам и автономным провинциям страны.

Осуществленные перемены

Еженедельные оценки рисков с использованием данного подхода регулярно применялись при мониторинге эпидемии на региональном уровне с 4 мая 2020 года по 24 сентября 2021 года. Инструмент обеспечил надежную оценку того, когда и где может произойти быстрое увеличение спроса на медицинские услуги, если меры по борьбе или смягчению последствий не будут усилены в течение следующих 3 недель.

Выводы

Несмотря на то что система работала эффективно, включение инструмента для оценок рисков в юридические постановления ограничивало его гибкость, поскольку показатели не могли быть изменены без изменения закона. Относительная сложность инструмента, невозможность проверки в реальном времени и его использование для определения ограничений создают проблемы коммуникации.

Introduction

In its first response to the emerging epidemic of coronavirus disease 2019 (COVID-19), the Italian government implemented national restriction measures with strict physical distancing and restrictions on public movements 1 that were maintained from March to May 2020. 2 Although followed by a rapid decrease in transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 1 the control measures had an adverse impact on the economy and society. 3 To continue and further improve Italy’s preparedness and response capacity, 4 we used an adapted version of the European Centre for Disease Prevention and Control (ECDC) rapid risk assessment tool 5 , 6 to conduct weekly subnational assessments using data from public health intelligence.

Italy’s health-care system is decentralized at regional level across 21 regions and autonomous provinces that largely differ in terms of size, population density and age structure as well as the financing and delivery of health care. 7 The infectious diseases surveillance system uses consistent data collection methods across the country, with local health units collecting notifications from clinical services and investigating cases. Local health units transfer the data to regional and autonomous provincial authorities who verify and notify cases to the Ministry of Health and to the Italian national institute of health ( Istituto Superiore di Sanità ; the national centre for research, control and scientific advice on public health).

We designed the Italian COVID-19 risk assessment tool to provide reliable assessments in the different regional contexts. The risk assessments produced for each region and autonomous province were initially used to informally assist regional epidemic responses. Subsequently, in October 2020 the assessments were formally integrated as part of a flexible COVID-19 prevention and control response strategy for the autumn to winter 2020 season. 4

From the beginning of November 2020, 8 the Italian government implemented laws to define the need for strengthening or easing of regional interventions to control the epidemic, according to fixed parameters. Until May 2021, the weekly regional risk assessments became one of the parameters used to define the level of restrictions to be implemented. We have shown the effectiveness of this approach in a previous publication. 9 Higher immunization coverage has decreased the impact of COVID-19 in terms of severe disease and death, and therefore the legal parameters used for mitigation of the pandemic were changed and risk assessments were no longer used. However, these assessments are still produced to date with the same method to monitor the epidemic and are published online. 10

The aim of the risk assessment was to provide a weekly overall categorization of the risk of an uncontrolled and unsustainable SARS-CoV-2 outbreak in each of the 21 Italian regions and autonomous provinces. As per ECDC guidance, we defined risk as a combination of the probability of the health threat and its impact, 5 , 6 described using consolidated risk categories (low, moderate, moderate with high probability of evolving to high, and high). We first assessed the probability and impact separately and then combined them to provide an overall risk, which we then adjusted in relation to an assessment of the health system’s resilience. This resilience element was an innovation not included in the original ECDC framework (see the authors’ data repository). 11

The three components of the Italian risk assessment tool therefore were: (i) probability (evidence of increased transmission, as a proxy for increasing probability of uncontrolled SARS-CoV-2 transmission in a region or autonomous province); (ii) impact (evidence of unsustainable burden of COVID-19 cases on hospital services); and (iii) resilience (capacity of the public health system to withstand the burden of the pandemic and maintain its functions within the test, track and trace strategy).

We used three sets of quantitative indicators to assess these components ( Table 1 ). 12 The first set (data quality output indicators) defined the minimum levels of data completeness to allow the risk assessment; the second set (resilience output indicators) monitored the resilience of public health services in maintaining high levels of testing and contact tracing; the third set (probability and impact result indicators) monitored the probability of an uncontrolled spread of SARS-CoV-2 and of an unsustainable impact of COVID-19 on hospital services. For each indicator, we defined thresholds for alerts.

COVID-19: coronavirus disease 2019; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

a A value of at least 50% of cases with increasing trends was considered acceptable between 4 May and 25 May 2020.

b We have previously described the details of the calculations. 1

c Net reproduction number is the transmissibility potential of the disease at a given time t of the disease. 1

Notes: Cases refer to people with a laboratory confirmed diagnosis of SARS-CoV-2 infection reported to the national surveillance system. Translated with minor adaptations from the Decree of the Italian health ministry, 30 April 2020. 12 The table only includes the non-optional indicators reported in the original ministerial decree as the other indicators were not relevant either because they were never compiled or because they just had a complementary role to the non-optional indicators here reported.

We assessed probability and impact separately using two dedicated algorithms each composed of three trigger questions requiring a yes/no answer. The first two questions in each algorithm were quantitative while the last question was qualitative. 11 We compiled the quantitative questions using data from the described indicators ( Table 1 ). To answer the qualitative questions, we activated the national event-based surveillance system 13 and received weekly declarations from regional public health authorities. More specifically, regional authorities declared respectively if an uncontrolled SARS-CoV-2 transmission that could not be managed locally, or if new clusters of infection in vulnerable settings, were occurring. 11 Once the assessment of the two algorithms for probability and impact was concluded, a first risk level was defined. We then looked at the resilience indicators. If we detected multiple alerts from the resilience indicators, we automatically scaled up the initial risk to the next risk level.

Between 4 May 2020 and 24 September 2021, the Italian national institute of health performed 71 weekly risk assessments. 10 Each assessment reported an updated classification of risk for each Italian region or autonomous province. As shown in Fig. 1 , the risk assessments captured regional risk heterogeneities and were consistent overall with the national epidemic curve. In the data repository, 11 graphs illustrate how the level of risk assigned was accurate in signalling when increases in the incidence of laboratory-confirmed severe and lethal infections were expected to occur within 3 weeks in the absence of additional control or mitigation measures.

Epidemic curve and weekly risk assessment of the COVID-19 epidemic by region and autonomous province, Italy, 4 May 2020 to 27 September 2021

In its early implementation, during low viral circulation in spring to summer 2020, the risk assessment system was very sensitive to localized clusters with limited cases, especially in smaller regions or autonomous provinces. The risk assessment therefore changed occasionally from low to moderate and then back to low as the clusters were contained. These findings, although consistent with the data, were misinterpreted as false alarms and led to some initial concern and distrust in the method among subject-matter experts. As the indicators could not be changed without changing the law, we solved these initial issues by clarifying concerns with public health officials without modifying the risk assessment tool.

Subsequently, the perceived complexity of the tool and the fact that risk assessments always addressed the previous week (too delayed) were criticized. 14 The net reproduction number (Rt), which reflects the transmissibility of the disease, was also debated and for similar reasons. Even though the risk assessment tool only gave a very limited weight to Rt ( Table 1 ), this controversy targeted its overall validity.

The weekly publication of the risk assessment findings 10 became a contested topic in the media, 14 increasingly so between November 2020 and May 2021, when higher risk was automatically associated by law with the enforcement of more severe restrictions to control the spread of the virus. Especially during the autumn to winter 2020 peak of COVID-19 cases, criticism of the assessment system expressed through the media increased, and numerous legal actions were started by representatives of different interest groups and organizations. However, to date, none of the legal actions have led to a re-evaluation of the published risks. Strategies that we adopted to improve public understanding included a weekly presentation of the risk assessments in a press conference and the production of releases and frequently asked questions pages on institutional websites. 15 The assessment method became less debated after its automatic impact on decision-making stopped.

During a protracted outbreak, ensuring that control measures against the spread of disease are proportionate to the risk is important to limit an unwarranted impact on the economy and on the overall well-being of the population. Ensuring accountability and transparency to the general population is also needed.

The risk assessment system supported decision-making in Italy by effectively anticipating when the disease outbreak was expected to rapidly worsen, harnessing existing data flows at national and subnational level. The system operated without dedicated funding but, despite requiring a large amount of staff time, was sustainable in the medium term without any disruption in the weekly production of updated risk assessment reports.

Continuous communication between the experts at the national institute of health and public health officials across all the regions and autonomous provinces made sure that the assessments reflected what was happening locally each week while supporting the cohesion of the public health network. Also, different data sources and multiple indicators were helpful in maintaining the robustness of the assessment during increased transmission.

The main challenge in conducting the risk assessments during the pandemic was related not to the method’s performance but to difficulties in public communication. While we addressed initial misunderstandings among public health officers through technical discussions, the situation changed when the risk assessments started directly impacting restrictions and, consequently, peoples’ daily lives and livelihoods. Criticism of the risk assessment tool (too complex) or of specific parameters (too delayed) stopped being a technical discussion among subject-matter experts and became a contested topic for decision-makers and the general public.

A similar approach to capturing the components of probability, impact and resilience could be adopted in different countries by adapting existing data flows and deploying available human resources. However, we learnt that to increase the acceptability of risk assessment tools like the one described, robustness in performance is not enough ( Box 1 ). Communication issues in applying risk assessment tools should be anticipated during an emergency. Approaches to resolve these issues need to be designed with communication experts, alongside the development of the risk assessment tools, to ensure that the acceptability of the risk assessments is not damaged by controversies driven by misunderstandings.

Box 1

Summary of main lessons learnt.

• Decision-making during large-scale disease outbreaks can be supported by robust mixed-method risk assessments informed by public health intelligence and existing surveillance systems without dedicated funding.
• Continuous communication with health officials across administrative levels ensures that assessments reflect what is happening in the field and supports the cohesion of the public health network during an emergency.
• Communication issues in applying risk assessment tools should be anticipated during an emergency and approached using dedicated communication tools.

Acknowledgements

The Italian COVID-19 monitoring group consists of Stefania Melena, Michele Labianca, Anna Domenica Mignuoli, Pietro Buono, Maria Giovanna Mattei, Paolo Giorgi Rossi, Fabio Barbone, Francesco Vairo, Paola Scognamiglio, Filippo Ansaldi, Lucia Di Furia, Francesco Sforza, Pier Paolo Benetollo, Pier Paolo Bertoli, Chiara Pasqualini, Lucia Bisceglia, Marcello Tidore, Salvatore Scondotto, Enrica Ricci, Mauro Ruffier, Filippo Da Re, Valentina Marziano, Filippo Trentini, Alessia Rapiti, Stefano Marro, Massimo Fabiani, Maria Fenicia Vescio, Matteo Spuri, Daniele Petrone and Marco Tallon. Flavia Riccardo and Giorgio Guzzetta contributed equally to this publication.

Competing interests:

None declared.

Operational guidance on rapid risk assessment methodology SHPN guide to using the UK guidelines

• Conditions and diseases
• Health protection

Open contents

This Scottish Health Protection Network (SHPN) guide should be used alongside the UK guidance that it relates to.

This guide covers ECDC 'Operational guidance on rapid risk assessment methodology'.

This UK guidance has been approved for use in Scotland by the SHPN Guidance Group (SHPN-GG).

Intended audience

This guidance is for health protection professionals only.

Other people who require advice should contact their local health protection team .

What the guidance covers

This guideline develops a methodology for rapid risk assessments undertaken in the initial stages of an event or incident of potential public health concern.

It describes an operational tool to facilitate rapid risk assessments for communicable disease incidents at both Member State and European level.

• Next page: SHPN recommendations from Operational guidance on rapid risk assessment methodology - version 1

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• Published: 14 September 2024

Static analysis-based rapid fire-following earthquake risk assessment method using simple building and GIS information

• Jaedo Kang 1   na1 ,
• Taewook Kang 2   na1 ,
• Kihak Lee 3 ,
• Hyewon Kim 4 &
• Jiuk Shin 2  

Scientific Reports volume  14 , Article number:  21492 ( 2024 ) Cite this article

Metrics details

• Civil engineering
• Engineering

After the occurrences of large-scale earthquakes, secondary damage (e.g., fire following earthquake) can result in tremendous losses of life, properties, and buildings. To reduce these disaster risks, fire following earthquake assessment methods composed of ignition and fire-burned rate estimation models have been utilized. However, previous methods required for large amounts of building and GIS information, and complex modeling and analysis processes, leading to significant time consumption. This paper proposed a static analysis-based rapid fire following earthquake assessment method using simple information and implemented it in Pohang City, South Korea. Based on previous studies, the best-fit model for the ignition rate estimation was selected, and a cluster-based fire-burned rate estimation model was developed using simple building information (e.g., construction year, building occupancy, story, and total floor area) from the public building database (e.g., building registration data). For the fire-burned rate estimation model, fire-resistant structure types were defined using simple building information, and this was utilized to generate clusters of buildings at a regional level by comparing fire-spread distances for each fire-resistant structure type with adjacent distances among the buildings. This proposed method was applied to Pohang City, South Korea, and validated as follows: (1) the selected ignition rate model predicted similar ignition numbers to the actual reported number (actual number of ignitions = 4 vs. predicted number of ignitions = 3), and (2) the fire-burned rate model estimated fire-burned areas with a marginal difference compared to the fire spread simulation (fire-burned area using the proposed model = 13,703.6 m 2 vs. results of fire spread simulation = 16,800.0 m 2 , with an error of approximately 18%).

Introduction

In November 2017, a 5.4-magnitude earthquake hit Pohang, in South Korea, resulting in the first recorded fire following an earthquake in Korea; this was officially documented by the Ministry of Interior and Safety in its earthquake damage report 1 . This suggests that South Korea, which lacks a system for predicting and managing earthquake damage, faces the possibility of large-scale regional secondary damage from earthquakes, such as fire following earthquakes (FFEs) leading to the spread of fire. All abbreviations used in this paper are summarized in Table A. After an earthquake, the fires leading to the earthquake can get ignited simultaneously in multiple locations, resulting in greater losses of life, property, and buildings than through direct damage from the earthquake itself because immediate response and suppression of fires caused by earthquakes can be challenging due to obstacles created to firefighting. To address the FFE risk, the U.S. and Japan have developed methodologies for evaluating the same based on actual data related to fires caused by earthquakes in the past, and more detailed information of FFE risk assessment methodologies can be found in Lee et al. 2 .

The FFE risk assessment methods proposed by previous studies are based on ignition rate and fire-burned rate (or fire-spread rate), and involve calculating a building's fire-burned rate, based on the probability and location of ignition caused by an earthquake. The ignition rate is the number of ignitions per unit building area after an earthquake has occurred, and ignition rate estimation models have been developed by analyzing ignition and earthquake data. The HAZUS-MH earthquake model 3 from the Federal Emergency Management Agency (FEMA) utilizes peak ground acceleration (PGA) to estimate the number of ignitions per unit building area. The earthquake model used in Japan 4 estimates the ignition rate by considering factors, such as earthquake intensity, building occupancy, and season. The fire-burned rate is the number or area of buildings consumed/affected by fire. Methods for calculating the fire-burned rate include dynamic fire simulations, regression model-based static fire-spread analyses, and clustering techniques. Dynamic fire simulations include the method utilized in FEMA's HAZUS-HM earthquake model 5 and fire-spread simulation based on the Tokyo Fire Department (TFD) model; these two models calculate the fire-burned rate differently. The former is based on the Hamada model 6 , 7 and assesses completely burned structures using fire simulation. Fire-spread simulation 8 used in Tokyo, Japan, and takes into account factors, such as the fire-resistant structure type of buildings, wind speed, and burning velocity to calculate the fire-spread rate, which is used to estimate the extent of burned structures in the evaluation area. Static fire-spread analysis can calculate the fire-burned rate using unburned area ratio and covering volume fraction (CVF) associated with the density of buildings. Other methods 4 , 9 , 10 , 11 , 12 exist for calculating the fire-burned rate from wood building coverage ratio associated with the spread of fire. The methodology 13 applying clustering technique involves calculating the regional fire-burned rate using the clustering technique proposed by Kato et al. 14 . Clustering means forming groups of buildings by comparing the distance among neighboring buildings with the fire-spread distance determined from the fire-resistant structure type of the building. The existing methods (HAZUS-MH model in the U.S. and earthquake models in Japan) for FFE risk assessment have been validated with high accuracy because they utilize various information such as building information (e.g., occupancy, total floor area, story, etc.) and weather conditions (e.g., wind speed, wind direction, humidity, etc.) along with GIS-based step-by-step modeling and analysis processes. However, these methodologies require the prior acquisition of large amounts of data, and the modeling and analysis processes make the evaluation process complex and time-consuming. Owing to these limitations, a new methodology is needed to rapidly assess FFE risk using simple information.

This study proposed a FFE risk assessment methodology that can rapidly evaluate using simple building and geographic information system (GIS) information without complex modeling and interpretation processes. As a first step, the calculation methods for ignition rate and fire-burned rate utilized in the FFE risk assessment methods proposed by previous studies were analyzed. Then, a method was proposed to calculate the ignition rate determined from the earthquake intensity and fire-burned rate simulated using building information, such as year of construction, number of floors, building use, structural material, and total floor area. The FFE risk assessment method proposed in this study was implemented to Pohang, South Korea in 2017, shown in Fig.  1 , and validated by comparing it with fire-spread dynamic simulation results.

Map of Pohang, South Korea (URL: https://www.qgis.org ).

Previous FFE risk assessment methods

This section presents an analysis of the existing FFE risk assessment methods to propose a methodology for quickly evaluating FFE risk using simple information related to buildings, such as construction year, stories, building occupancy, structural material used, and total floor area. Furthermore, the process behind the proposed FFE risk assessment is explained.

The conventional FFE risk assessment methods are commonly based on calculating ignition and fire-burned rate. The ignition estimation models include regression analysis to estimate the ignition rate based on number of ignitions and earthquake intensity data 15 , 16 , 17 , 18 , and the model to estimate the number of ignitions using earthquake and fire-related data (earthquake intensity, building occupancy, and season) 19 . The fire-burned rate estimation models include methods that use dynamic fire simulations 5 , 8 , regression model-based static fire-spread analyses 4 , 10 , 11 , 12 , 13 , 14 , and clustering techniques. Dynamic fire simulations involve calculating the number of fully burned structures using FEMA's HAZUS-MH earthquake model 5 , which itself is based on the Hamada 6 , 7 , and TFD models, which consider the fire-resistant structure type, wind speed, and burning velocity to calculate the fire-spread rate. This method estimates the burned area inside the evaluation region through fire-spread simulation 8 . Static fire-spread analysis methods involve calculating the fire-burned rates using the unburned area ratio–fire-burned rate function, wooden building coverage ratio–fire-burned rate function, and CVF–fire-burned rate function developed using actual FFE data, as well as a clustering method proposed by Kato et al. 14 to calculate fire-burned rate. Firstly, the unburned area ratio–fire-burned rate function assumes that areas with an unburned area ratio of 70% or more do not experience fire spread, and therefore the fire-burned rate in those areas is considered to be 0%. The wooden building coverage ratio–fire-burned rate function determines the fire-burned rate of a specific area based on the wooden building coverage ratio of that area. Areas with a wooden building coverage ratio above 40% are considered to have a high risk of fire, while those with a wooden building coverage ratio below 20% are considered to have a low risk of fire. However, the unburned area and wooden building coverage ratios cannot be applied to small-scale areas and have the drawback of not reflecting the effectiveness of buildings with high fire-resistant capabilities, such as quasi-fire-resistant and fire-resistant structures. To address these issues, the relationship between CVF and fire-burned rate was developed to evaluate the fire-burned rate. This methodology evaluates the distance between buildings as a buffer to mitigate the spread of fire, calculates the CVF, and determines the fire-burned rate of a building based on the average fire-burned rate derived from the CVF. The method of applying clustering techniques involves creating clusters, as proposed by Kato et al. 14 for the evaluation area and assessing the fire-burned rate for that area. Next, the fire-spread distance and proximity of buildings based on the fire-resistant structure type are used to create clusters, and the CVF and average fire-burned rate are calculated to determine the fire-burned rate of buildings at a regional level (or larger-scale).

Since the previous FFE risk assessment methods require complicated modeling and simulation processes with large amounts of data, they result in time-consuming tasks. To overcome the limitations and develop the rapid FFE method, this study investigated the characteristics and limitations of ignition rate and fire-burned rate estimation models proposed by previous researchers. This study examined the applicability of a new FFE risk assessment methodology using Korean public databases. The characteristics, limitations, and applicability of each model are summarized in Table 1 . Based on the reviewed content, this study proposes the following FFE risk assessment methodology. An ignition rate model was proposed that calculates the number of ignitions by building area using cluster-based PGA in conjunction with FEMA's HAZUS-MH earthquake model 3 and the cluster method of Kato et al. 14 . Additionally, a fire-burned rate model was proposed based on simple information of buildings, such as construction year, stories, building use, structural materials, and total floor area, in conjunction with the cluster concept of Kato et al. 14 . The proposed FFE risk assessment method, whose steps are listed below, was applied to evaluate the regional FFE risk in Pohang City where the severe earthquake damage of buildings occurred in November 2017 in South Korea.

Obtain building registration data for Pohang City and preprocess the raw data.

Classify the buildings based on their fire-resistant structure type, using the building registration data.

Estimate the fire-spread distance by fire-resistant structure type and create clusters by comparing GIS-based distances between the buildings.

Determine the number of ignitions using a model for calculating the ignition rate within the clusters.

Apply a model to estimate the fire-burned rate within each cluster to calculate the number of burned buildings and burned area within that cluster.

Finally, evaluate the FFE risk at a regional level.

This assessment methodology does not require large amounts of data compared to existing FFE risk assessment methodologies because it evaluates FFE risk using simple building information. Additionally, since it does not involve step-by-step modeling and analysis processes, the evaluation process is straightforward and allows for rapid FFE risk assessment. This can contribute to making rapid decisions for responding to FFE risks.

Classification of buildings based on fire-resistant structure type

This section describes the classification process of the fire-resistant structures type of buildings in Pohang to generate fire-resistant characteristic-based clusters proposed by Kato et al. 14 . This involves grouping or clustering buildings with similar fire-resistant structure types and proximity distances based on the fire-spread distance. However, the building registration data in South Korea does not classify the buildings based on their fire-resistant structure types in its building-related databases. Hence, it is not possible to evaluate the fire-spread distance based on the fire-resistant structure type. This study developed a methodology to classify fire-resistant structure types of buildings using simple information based on the amendments in building laws, as shown in Fig. 2 . On June 30, 1992, Article 56 'Fire-resistant Structures' of the Enforcement Decree of the Building Act in South Korea was amended for the first time. Using this date as the reference date, the classification methodology determines the fire-resistant structure type for buildings constructed before this date based on the structural materials used in the structural frames. For buildings constructed after the reference date, the classification is done by comparing the construction year, story, building occupancy, structural materials, and total floor area.

The proposed method for classifying fire-resistant structure type of buildings using simple information.

The aforementioned data for buildings in Pohang City were obtained. The analysis revealed that up to 50.6% of the necessary data were missing or incomplete, which could impact the assessment of fire spread in the FFE risk evaluation process, especially in determining the spread to adjacent structures; thus ensuring the completeness of data acquisition was essential and a backtracking algorithm was developed to trace the missing data elements in the building registration data, as shown in Fig. 3 . This methodology has been developed by incorporating statistical results from public databases provided by the government related to buildings and land, and the characteristics of buildings. When applied to missing data, it can generate highly reliable information. To obtain complete data, the algorithm given in Fig. 3 was implemented to the building registration data of Pohang City. Among the data, the building coverage and floor area ratio, which had the highest missing rates of 50.6%, were reduced to 0% missing rate through the application of the data backtracking algorithm. Table 2 presents the backtracking results for the data considered for classifying the fire-resistant structure types of buildings. The resulting fire-resistant structure types of buildings in Pohang City included timber structures, fire-preventative timber structures, quasi-fire-resistant structures, and fire-resistant structures). The results are summarized in Table 3 , which reveals that among 89,708 buildings in Pohang, the fire-resistant buildings comprised 12,688 timber structures and 19 fire-preventative timber structures. The quasi-fire-resistant structures comprised 64,730 buildings, representing the highest proportion, while the fire-resistant structures comprised 12,298 buildings. The classification results of regional fire-resistant structure types in Pohang City are presented in Table B of the Appendix .

Backtracking algorithm of building registration data.

Application of the FFE risk assessment method

This section describes the process of evaluating the regional FFE risk by implementing the proposed FFE risk assessment method to Pohang City. The fire-resistance type-based clusters generated through comparing between the fire-spread distance and adjacent building distance are used to calculate the ignition rate and fire-burned rate for assessing the regional-level FFE risk. The explanation of the technical terms used in this section is summarized in Table A of the Appendix .

Creation of clusters based on fire-resistant structure types

This section describes the process of creating clusters in Pohang City using fire-spread distance according to the classification of buildings considering their fire-resistant structure types, which was presented in Section " Classification of Buildings Based on Fire-resistant Structure Type ".

The concept proposed by Kato et al. 14 involves grouping buildings with similar fire-resistant structure types based on comparing the fire-spread distance and adjacent distances according to the fire-resistant structure types, as shown in Fig.  4 . When comparing the fire-spread distance ( \({d}_{i}\) ) of buildings based on the adjacent distance ( \({n}_{i}\) ) between them, and their fire-resistant structure types, the overlapping buildings form a single cluster. In this study, \({n}_{i}\) was calculated based on the polygon shape data of buildings in the GIS. Furthermore, \({d}_{i}\) was calculated using Eqs. ( 1 )–( 4 ), which are formulae based on the fire-resistant structure type of the building 6 , 20 :

where, a is the length of one side of the building.

Example of GIS information and fire-spread distance based on clusters.

A total of 5,946 clusters were created by grouping buildings with similar fire-resistant characteristics based on the values of \({d}_{i}\) calculated from Eqs. ( 1 )–( 4 ) and \({n}_{i}\) calculated using GIS.

Calculation of number of regional ignitions

This section describes the process of calculating the number of regional-level ignitions using the ignition rate model, adopted in the FFE risk assessment method proposed in this study.

Ignition rate refers to an estimated number of ignitions per unit area of a building or per unit area after an earthquake has occurred. One model estimates the number of ignitions using regression with earthquake intensity data and other data related to earthquakes and fires 3 . The ignition rate model used in Japan 4 calculates the rate at which ignitions lead to fires by taking into account climatic conditions (e.g., season, wind speed, wind direction, and humidity), time, number of ignitions, first-fire-extinguishing rate, and firefighting force. Even though the ignition rate model, considering the aforementioned factors, results in high accuracy predictions, the use of the model is limited due to the lack of related data existing in Korea. Therefore, the prediction models based on earthquake intensity versus number of ignitions, proposed by other researchers 3 , 15 , 16 , 17 , 18 , 21 were considered in this study. To select the most suitable model from the above, the correlation of the proposed formula for the number of ignitions 3 , 16 , 18 , 21 was analyzed with 1,435 ignition data elements. Figure  5 depicts the correlation between the data and the number of ignitions as obtained from the formula. FEMA's HAZUS-MH model 5 had the highest R-squared (R 2 ) value of 0.425, while Ren and Xie's model 16 had the lowest mean square error (MSE) of 0.025. The ignition rate estimation models considered in this study were examined in Table 4 . Despite the relatively high MSE of 0.093, it was determined that the model from FEMA's HAZUS-MH (2020) was appropriate because the ignition rate estimation model has the highest correlation (R 2  = 0.425) with the data among the considered models. The comparison of the correlation between the ignition rate estimation models considered in this study and the actual fire data is summarized in Table 4 .

Correlation between actual fire data and existing ignition rate estimation models.

This study utilized the PGA of clusters generated in Pohang City to calculate the number of cluster-based ignitions using Eq. ( 5 ) to estimate the number of ignitions based on earthquake intensity:

where, x = PGA and y = number of ignitions per total floor area of 1,000,000 \({ft}^{2}\) . The results are summarized in Table 5 .

For calculating the number of regional-level ignitions based on clusters, the buildings were classified into 29 administrative regions using the administrative codes of the buildings included in the clusters. The administrative code is defined as "dong," which is the unit that separates regions in South Korea, and it is used to represent the administrative districts of the South Korea. The number of regional-level ignitions was calculated by summing the number of ignitions for all the clusters in each region, and the results are summarized in Table 6 . The number of regional-level ignitions ranged from 0.011 to 1.678, with a total of 2.786 (nearly 3) in Pohang city. A comparison between the numbers of ignition reported after an actual Pohang earthquake in 2017, which was four incidents 1 , and the number as predicted by the model demonstrates a good agreement between the two results.

Calculation of regional CVF and average fire-burned rate

This section describes the process for calculating the cluster-based CVF and average fire-burned rate using the model proposed in this study for FFE risk assessment.

CVF is defined as the area excluding the fire area, as presented in Fig.  6 . The fire area is defined as the area that includes the range of fire-spread distance, determined by the building area and fire-resistant structure type. The concept of CVF was developed in Japan to assess regional FFE risk, and represents the area excluding the fire-spread area per unit regional area, as shown in Eq. ( 6 ) 25 :

where the buffer area is calculated using Eq. ( 7 ) given below 25 .

Combustion range by fire-resistant structure type of buildings.

In Japan, Eq. ( 7 ) is calculated to take into account the varying fire area based on the fire-resistant structure type of the building. Here, one issue with Eqs. ( 6 ) and ( 7 ) is that the area outside the cluster boundaries is not accounted for. Therefore, this study aimed to consider the area beyond the cluster boundaries as well, thus enlarging the peripheral land area. The newly proposed CVF equation is as follows:

where \(n\) is the peripheral land amplification coefficient.

To expand the area of the peripheral land, \(n\) was set to 1.1 in Eq. ( 8 ).

Subsequently, the average fire-burned rate used to calculate the fire-burned rate (total number of burned buildings and burned area) by regional FFE is defined as the rate at which buildings are lost to fire when ignition occurs in a cluster and is calculated using Eq. ( 9 ).

When the CVF of a cluster is less than or equal to 0.1 in Eq. ( 9 ), the average fire-burned rate of the cluster is approximately 1. The loss rate of buildings included in the cluster is 100%. When a cluster's CVF is more than 0.5, the cluster's average fire-burned rate is approximately 0.1, indicating a loss rate of approximately 10% for the buildings in the cluster. Using Eqs. ( 8 ) and ( 9 ), the CVF and average fire-burned rate were calculated for 5,946 clusters in Pohang City. Table 7 summarizes the number of structures, CVF, and average fire-burned rate of clusters in Pohang City.

Assessment of regional-level FFE risk

This section describes the evaluation of regional-level FFE risk by utilizing the cluster unit average fire-burned rate explained in " Calculation of regional CVF and average fire-burned rate ", which is used to determine the regional-level fire-burned rates (total number of burned buildings, total burned building area, and total burned floor area). Based on the regional-level fire-burned rates, the burned rates caused by FFE (number of burned buildings, burned building area, and burned floor area) were calculated to assess regional-level FFE risk.

The fire-burned rates used in the assessment of regional FFE risk refer to the number of burned structures or area caused by FFE. The fire-burned rates are calculated by multiplying the average fire-burned rate by the number of buildings, building area, and total floor area, respectively.

To calculate the fire-burned rate used in the regional FFE risk assessment, 29 areas were classified based on the administrative codes of the buildings included in the cluster. Then, the total number of burned buildings, burned building area, and total burned floor area were summed up at the regional level using Eqs. ( 10 )–( 12 ):

where, \({F}_{n}\) is the total number of burned buildings in regional level; \({F}_{A}\) is the total burned building area in regional level; \({F}_{TFA}\) is the total burned floor area in regional level; \(i\) is an index for regional clusters ( \(i=1, 2, 3,\text{ etc}.)\) ; \({FS}_{i}\) is the average fire-burned rate in regional cluster \(i\) ; \({n}_{i}\) is the number of buildings in regional cluster \(i\) ; \({A}_{i}\) is the total building area of regional cluster \(i\) ; and \({TFA}_{i}\) is the total floor area in regional cluster \(i\) .

Table 8 presents the information related to the local unit buildings in Pohang City, including the CVF and fire burned rates (total number of burned buildings, burned building area, and total burned floor area). The number of buildings, building area, and total floor area in Pohang City were estimated to be 61–1938, 7149–2,949,187 m 2 , and 13,343–3,073,93 m 2 , respectively. The results for the CVF and average fire-burned rate were calculated to be 0.311–0.808 and 0.046–0.321, respectively. The total number of burned buildings, burned building area, and total burned floor area were calculated using Eqs. ( 10 )–( 12 ) to be 4.03–598.86 buildings, 384.86–167,182.63 m 2 , and 843.70–173,476.81 m 2 , respectively. Subsequently, an analysis was conducted on the total number of burned buildings, burned building area, and total burned floor area in Pohang City, wherein it was found that Pohang City North C (N-C) had the highest total number of burned buildings at 598.86, while Pohang City North K (N-K) had the lowest at 4.03. In the case of burned building area and total burned floor area, Pohang City South A (S-A) had the highest values with 167,183 and 173,477 m 2 , respectively, while Pohang City N-K had the lowest values with 384.86 and 843.70 m 2 , respectively. The fire-burned rate of Pohang City N-C was expected to be the highest owing to its high regional number of structures (1,938) and average fire-burned rate (0.274). However, the actual fire-burned rate calculation showed that the burned building area and total burned floor area were highest in Pohang City S-A, wherein the factory areas account for approximately 82% of the total structures, resulting in a dense concentration of factories. Here, despite a low number of buildings (592) and an average fire-burned rate of 0.049, the burned building area and total burned floor area were the highest at 2,949,186 and 3,073,939 m 2 , respectively. In the case of Pohang City N-K, the low number of buildings included in the cluster and average fire-burned rate, which were 77 and 0.045 respectively, resulted in the lowest fire-burned rates (number of burned buildings, burned building area, and total burned floor area).

The regional FFE risk was assessed by using the regional fire-burned rate (total number of burned buildings, burned building area, and total burned floor area), and ratio of buildings lost due to regional FFE (rate of total number of burned buildings and the rate of burned area). Therefore, this study included evaluation indicators and calculation formulae to assess area FFE risk. The rates of the total number of burned buildings, burned building area, and total burned floor area represent the rates of these variables due to regional FFE. These rates were calculated using Eqs. ( 13 )–( 15 ):

where \({R}_{n}\) is the rate of the number of burned buildings in regional level; \({R}_{A}\) is the rate of burned building area in regional level; \({R}_{TFA}\) is the rate of total burned floor area in regional level; S is the regional unit area.

Equations ( 13 )–( 15 ) were used to calculate the rates of the number of burned buildings, burned building area, and total burned floor area due to regional FFE. Table 9 summarizes the results of the regional fire-burned rates, and the corresponding numbers per unit regional area.

The rates of burned buildings, building area, and total floor area in Pohang City due to FFE were calculated to be 0.01–0.03%, 0.01–2.39%, and 0.01–4.82%, respectively. Subsequently, an analysis was conducted on the above rates by region. These were found to be highest in Pohang City N-C at 0.03%, 2.39%, and 4.82%, respectively, while, Pohang City North M (N-M) had the lowest value of 0.01% for all the three parameters. In Pohang City N-M, the fire-resistant structure type had the lowest rates at 1.12%, while the timber structure type had the highest proportion at 40.45%, indicating that the risk level due to fire is expected to be the highest for timber structures. However, the actual FFE risk assessment results were rated as the lowest. The rate of fire-resistant structures in Pohang City N-C was found to be 13.85% higher than in Pohang City N-M. Furthermore, the low rate of timber structures at 14.49% indicates an expected low FFE risk. However, it was rated the highest in the actual evaluation results. To ascertain the causes of these results, data related to the regional FFE risk assessment results were analyzed, and it was found that FFE risk is associated with the density of buildings at the regional level and proportion of fire-resistant structure types.

To understand the relationship between the FFE risk, building density, and proportion of fire-resistant structure types, the regional FFE risk assessment results were analyzed along with building density and the trend of fire-resistant structure types. In Pohang City N-M and North B (N-B), the proportion of fire-resistant structures in the local building units was lowest at 1.12% and 1.52%, respectively, and the timber structures were expected to have the highest FFE risk at 40.45% and 40.48%, respectively. However, the actual FFE risk was evaluated as the lowest due to the lowest density of such buildings at 0.10% and 0.24%, respectively (In the case of Pohang City N-B, the FFE risk was calculated at 0.01% for the number of burned buildings, building area, and total floor area). In comparison to Pohang City N-M and N-B, Pohang City N-C and North L (N-L) exhibited higher proportions of fire-resistant structures at 13.85% and 17.84% respectively, and lower ratios of timber structures at 14.69% and 2.57% respectively, indicating a lower risk of FFE, as expected. However, the density of buildings at the local level was highest at 25.19% and 35.04% respectively, resulting in the highest evaluated FFE risk (in the case of Pohang City N-L, the rates of burned buildings, building area, and total floor area as FFE were calculated as 0.01%, 0.76%, and 1.61%, respectively).

Figure  7 shows the results of applying the proposed FFE evaluation methodology to Pohang and evaluating the density of buildings, proportion of fire-resistant structure types, and risk assessment due to FFE for the four locations mentioned earlier (Pohang City N-C, N-L, N-M, and N-B). Furthermore, in areas with similar building density, the FFE risk was determined by the proportion of fire-resistant structure types in buildings. Detailed information regarding the density of buildings at the regional level, proportion of fire-resistant structure types, and their relation to the FFE risk are presented in Table C of the Appendix . Based on the analysis of the evaluation of the regional FFE risk, it was confirmed that the FFE risk was influenced by the density of buildings at the regional level and proportion of fire-resistant structure types in a complex manner. Additionally, this approach allows users to understand the relationship between building characteristics and FFE risk and to determine the regional risk ranks from FFE. By utilizing the FFE assessment methodology proposed in this study, it is possible to develop preemptive measures for FFE risk, such as allocating additional firefighting resources to high-risk areas or determining priority response orders for regions to reduce casualties. This is expected to contribute to rapid and rational decision-making in policy formulation or urban planning.

Result of regional-level fire following earthquake risk assessment in Pohang (URL: https://www.qgis.org ). D density, R composition ratio of fire-resistant structures, T composition ratio of timber structures, F rate of the burned total floor area, number in () regional-level rank in Pohang.

• Fire-spread simulation

This section describes the validation of the proposed simple information-based FFE risk assessment method, by conducting fire-spread simulations, as shown in Fig.  8 , in an actual area Pohang City North O (N-O), where an earthquake occurred. This process also includes comparing with the FFE risk assessment results of Pohang City N-O presented in " Assessment of regional-level FFE risk ".

Process of fire-spread simulation.

The fire-spread simulation 8 calculates the burned area of a target region based on the fire-spread speed in terms of grid units of a certain size that the target region is divided into. The fire-spread rate is calculated taking into account the fire-resistant structure type of the building, wind speed, and burning velocity. This methodology involves a preliminary building modeling process for the target area and utilizes grid-based building information, thereby accurately representing the characteristics of the actual target area and achieving high accuracy in the results. However, the process of modeling the target area is time-consuming. Additionally, if the building density in the target area is low or the building information is missing, fire spread to adjacent buildings does not occur. Therefore, in this study, fire spread simulation was conducted for the N-O in Pohang City, where an actual earthquake had occurred. Considering that higher building density facilitates fire spread, the simulation was limited to the most densely populated urban area within the N-O in Pohang City. To calculate the fire-spread rate in grid units for the target region, it was divided into grids of a uniform size as shown in Fig.  9 a. Next, as shown in Fig.  9 b, a GIS program was used to generate GIS-based input data for the fire-spread rate by creating simulation input data for the fire spread. These data included proximity distance between the buildings, the length of one side of the timber structure buildings), and the length of one side of quasi-fire-resistant structure buildings. The generated input data for the fire-spread simulation, including the coordinates of the target region and fire-spread rate, are summarized in Table 10 .

Grid and input data for calculating fire-spread speed; ( a ) created grid in target region, ( b ) process of generating input data for calculating fire-spread speed (url: https://www.qgis.org ).

The fire-spread speed over time was calculated using the GIS-based input data, adjacent distance between the buildings, and length of one side of the timber structure, and Eq. ( 16 ) 8 :

where, \({V}_{0}\) is the initial burning velocity and \({V}_{f}\) is the final burning velocity.

The initial and final burning velocities used in Eq. ( 16 ) were calculated using Eqs. ( 17 ) and ( 18 ), respectively 9 :

where, \(\delta\) is the burning velocity.

The burning velocity used in Eq. ( 18 ) was calculated using Eq. ( 19 ) 9 :

where, a and b are the length of building; d is the adjacent distance of buildings; a ′, b ′, c ′, d ′ and j ′ are the area ratios of the building; \({V}_{w}, {V}_{c}, {V}_{m}\; and\; {V}_{j}\) are the burning velocities of the building; and \({V}_{nn}, {V}_{nc}, {V}_{cc}\; and\; {V}_{nj}\) are the burning velocities between the buildings.

Fire-spread simulations were conducted based on the grid unit fire-spread speed, calculated using Eqs. ( 16 )–( 19 ), and the results are presented in Fig.  10 . The simulation revealed that the burned area of the target location was 16,800 m 2 . The burned rates, which are the ratio of the burned area to the area of the target region or regional unit, were 1.65% and 0.02% for the fire-spread simulation target region and the regional-level, respectively. The fire-burned rate was calculated using Eqs. ( 20 ) and ( 21 ):

where, \({R}_{sim}\) is the burned rate per the area of target region; \({R}_{reg}\) is the burned rate per the area of administrative region; \(BA\) is the burned area derived through fire-spread simulation; \({S}_{sim}\) is the area of target region set for fire-spread simulation; and \({S}_{reg}\) is the area of administrative region.

Result of fire-spread simulation, ( a ) composition ratio of quasi-fire-resistant structures, ( b ) fire-burned area in target region (url: https://www.qgis.org ).

In this study, \({S}_{sim}\) and \({S}_{reg}\) were set to 1,020,100 m 2 and 105,720,000 m 2 , which are the area of the simulation target area and the area of Pohang City N-O, respectively. To validate the proposed FFE risk assessment method, the results for Pohang City N-O, calculated as per the process explained in " Assessment of regional-level FFE risk " were extracted for the selected area. The ratio of fire-burned area in the extracted region was compared with that from the fire-spread simulation and presented in Table 11 . The proposed FFE risk assessment method resulted in a burned area of 13,703.6 m 2 in the target region. The ratios of the fire-burned area computed from the proposed method to the target region area and the Pohang City N-O area are 1.34% and 0.01%, respectively. The differences of the fire-burned area between the fire-spread simulation and the proposed FFE assessment method for the target region and the Pohang City N-O were 18.43% and 18.24%, respectively. The variations between the proposed method and fire spread simulation were due to the differences in the main variables considered by each analysis method, which affected the fire-burned area. The results of the proposed method were governed by the characteristics and number of clusters, which were determined by the fire-resistant types and the adjacent distance between buildings. However, the results of the fire spread simulation highly depended on the building densities in the target region. The difference between the results obtained by applying the proposed FFE risk assessment method and fire-spread simulation is marginal. However, the FFE risk assessment methodology proposed in this study has only been validated in the N-O in Pohang City, where an actual earthquake occurred. To utilize this methodology for disaster preparedness in policy-making and urban planning, it is essential to apply and validate it in other regions and large cities beyond the N-O in Pohang City.

Conclusions

In this study, a new FFE risk assessment method was proposed to overcome the limitations of the existing methods and validate its feasibility. The ignition rate and fire-burned rate models of the FFE risk assessment methods proposed by previous studies were thoroughly analyzed. Based on the findings, a simple information-based FFE risk assessment method was proposed and applied to Pohang City, where an earthquake had occurred, to evaluate its regional FFE risk. Furthermore, the proposed method was validated by comparing it with the results of a fire-spread simulation. The key findings of this paper are as follows.

The FFE risk assessment methods proposed by previous studies require large amounts of information (e.g., actual data related to the building, earthquake, fire and weather conditions) and complex modeling methodologies (e.g., FEMA's HAZUS-MH earthquake model and TFD model-based fire spread simulation). In contrast, the proposed method allows for rapid evaluation using simple information (e.g., PGA, construction year, story, building occupancy, structural material, and total floor area) based on clusters. Additionally, this methodology can contribute to developing rapid and rational preparedness strategies for FFE risks in the context of policy-making and urban planning.

The public database established in South Korea does not differentiate the fire-resistant structure type of buildings. Therefore, a method for classifying the fire-resistant structure type of buildings was proposed based on simple information of building and the Building Act in South Korea. The proposed method enabled the classification of the building's fire-resistant structure types from construction year, story, building occupancy, structural material, and total floor area. Using this method, the fire-resistant structure types of buildings located in Pohang City were simply classified.

To validate the proposed FFE risk assessment method, the number of regional-level ignitions was evaluated in Pohang, South Korea. Using the provided ignition rate estimation model, the evaluation of the number of regional ignitions yielded approximately three incidents (2.786 incidents). This agreed well with the actual reported number of ignitions (four incidents) 1 in the actual Pohang earthquake.

Using the fire-burned rate estimation model proposed in this study, the regional FFE risk in Pohang City was evaluated. The areas with the highest building density, Pohang City N-C and N-L (25.19% and 35.04%, respectively) showed the highest risk levels (rates of the burned total floor area are 4.82% and 1.61%, respectively), while the regions with the lowest building density, Pohang City N-M and N-B (0.10% and 0.24%, respectively), were rated as having the lowest risk levels (rates of the burned total floor area are 0.01% in both regions). In regions with similar building densities (e.g., S-B and S-F in Pohang city, densities are 0.94% and 0.87%, respectively), a higher ratio of timber structures (25.80% and 23.02%, respectively) tended to be associated with a higher level of risk assessment (rates of the burned total floor area are 0.11% and 0.02%, respectively). The evaluation of regional FFE risk revealed that the FFE risk was influenced by the density of buildings and type of fire-resistant structure in a complex manner. This indicates that FFE risk can be varied depending on building characteristics. Utilizing the methodology proposed in this study, it can contribute to the development of rapid and rational disaster preparedness strategies, such as allocating additional firefighting resources to high-risk areas or determining priority response orders.

The FFE risk assessment method developed in this study was validated by conducting fire-spread simulations on Pohang City N-O from which the fire-burned area and fire-burned rate of the target region were calculated. The results obtained through the proposed FFE risk assessment method (total burned floor area, burned rate compared to the area of the target region, and burned rate compared to the area of Pohang City N-O were 13,703.6 m 2 , 1.34%, and 0.01%, respectively) were compared with the results from the fire-spread simulation (with the corresponding results being 16,800 m 2 , 1.65%, and 0.01%, respectively). It was confirmed that the discrepancy between the two was not significant. The fire-burned rate compared to the area of the target region and fire-burned rate compared to the area of Pohang City N-O showed differences of 18.43% and 18.24%, respectively. Thus, the FFE risk assessment method proposed in this study yielded results similar to those from the fire-spread simulation.

Based on a series of research processes, it was confirmed that the FFE risk assessment methodology proposed in this study allows for rapid evaluation compared to existing FFE assessment methodologies and produces reliable results for the N-O in Pohang City, where an actual earthquake occurred. However, the primary purpose of this study is to propose a methodology that can rapidly evaluate FFE risk using simple information. To utilize this methodology for disaster preparedness, it is essential to ensure the reliability of the data used in the evaluation process and to further demonstrate the methodology on various regions.

Data availability

The data used and/or analyzed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

This research was supported by a grant (2022-MOIS63-003(RS-2022-ND641021)) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety (MOIS, Korea).

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Kang, J., Kang, T., Lee, K. et al. Static analysis-based rapid fire-following earthquake risk assessment method using simple building and GIS information. Sci Rep 14 , 21492 (2024). https://doi.org/10.1038/s41598-024-72363-6

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Key concepts in rapid reviews: an overview

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Rapid reviews have gained popularity as a pragmatic approach to synthesise evidence in a timely manner to inform decision-making in healthcare. This article provides an overview of the key concepts and methodological considerations in conducting rapid reviews, drawing from a series of recently published guidance papers by the Cochrane Rapid Reviews Methods Group. We discuss the definition, characteristics, and potential applications of rapid reviews and the trade-offs between speed and rigour. We present a practical example of a rapid review and highlight the methodological considerations outlined in the updated Cochrane guidance, including recommendations for literature searching, study selection, data extraction, risk of bias assessment, synthesis, and assessing the certainty of evidence. Rapid reviews can be a valuable tool for evidence-based decision-making, but it is essential to understand their limitations and adhere to methodological standards to ensure their validity and reliability. As the demand for rapid evidence synthesis continues to grow, further research is needed to refine and standardise the methods and reporting of rapid reviews.

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The occurrence and spread of an animal health threat can be prevented when a timely assessment of the risk is carried out to inform prevention, response and control measures. These technical guidelines on rapid risk assessment (RRA) are designed as a simple and practical tool to be used by veterinary services to build risk assessment capacities and assist decision-makers in conducting qualitative RRA on the emergence, occurrence and/or spread of animal health threats. Using available evidence, data and information, a multidisciplinary team can conduct an RRA in a short time (within two weeks). The publication provides a simple and flexible methodology for conducting a RRA when facing a disease event. Eight steps in the RRA process are described and detailed examples are provided. The final outcomes of the RRA provide robust evidence and guidance for decision-makers in designing timely prevention, control and eradication measures that contribute to sustainable livelihoods, animal health, public health and enhanced food security.

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Safety Management of Marine Biotoxins in South Korea: Analytical Methods, Occurrence, and Risk Assessment

• Published: 11 September 2024
• Volume 59 , article number  41 , ( 2024 )

Cite this article

• Seongjin Hong 1 ,
• Hyun-Ki Hong   ORCID: orcid.org/0000-0002-6646-3417 2 &
• Kwang-Sik Choi 3  

Over the past few decades, various marine biotoxins, including paralytic shellfish poison, diarrheal shellfish poison, neurotoxic shellfish poison, and amnestic shellfish poison, have become international oceanographic concerns. These toxins are closely linked to global warming and the subsequent northward migration of toxic marine organisms, such as microalgae, fish, and benthic invertebrates, from tropical and subtropical regions. In South Korea, the bioaccumulation of marine biotoxins and incidents of seafood poisoning have also emerged as critical issues. Clear evidence indicates that the presence of toxic marine organisms in Korean coastal waters has increased, likely due to recent increases in seawater temperature. Since 2020, supported by the Ministry of Food and Drug Safety, the R&D project ‘Establishment of the Safety Management System for Marine Biotoxins’ has been carried out. This project aims to identify various regulated and unregulated marine biotoxins present in Korean coastal waters and seafood. This comprehensive project encompasses: (1) analytical methods, (2) causative organisms, (3) seafood contamination status, (4) novel and rapid detection method, (5) alternative toxicity testing method, (6) standard materials, and (7) risk assessment. The purpose of this special issue is to share the accumulated knowledge and technological advancements related to marine biotoxins by Korean researchers. The issue includes nine papers covering various types of marine biotoxins, as well as innovative bioassays and rapid detection kits. Additionally, it covers topics such as risk assessment and biotoxin management to ensure the safety of marine products.

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Marine biotoxins, produced by certain microalgal species of dinoflagellates and diatoms, pose serious health risks when these organisms are stressed by environmental factors such as changes in water temperature, salinity, and pH (Rigby et al. 2022 ). Biological stressors, including competition for nutrients and trace elements between species and the threat of predators, also promote toxin production (Tatters et al. 2013 ; Brandenburg et al. 2020 ). These biotoxins can accumulate in marine organisms such as bivalves, crustaceans, and fish, with accumulation patterns being species-specific and toxin-specific (Liu et al. 2019 ; Zhao et al. 2022 ). Poorly metabolized and excreted toxins can persist in organisms, including bivalves, for extended periods, sometimes several months (Kim et al. 2022 , 2023 ).

Shellfish toxins are categorized based on their symptoms into paralytic shellfish poison (PSP), diarrheal shellfish poison (DSP), neurotoxic shellfish poison (NSP), and amnestic shellfish poison (ASP) toxins (Chen et al. 2017 ). They are also classified based on chemical properties into hydrophilic and lipophilic toxins. Hydrophilic marine algal toxins include saxitoxin (STX) and gonyautoxin (GTX) (Gerssen et al. 2010 ). Lipophilic toxins include okadaic acid (OA), dinophysis toxin (DTX), yessotoxin (YTX), pectenotoxin (PTX), brevetoxin (BTX), azaspiracid (AZA), and cyclic imines (CIs) (Wang et al. 2015 ). Over recent decades, marine biotoxins have become an international oceanographic concern due to increasing reports of poisoning worldwide (Nicolas et al. 2017 ; Hallegraeff et al. 2021 ; Accoroni et al. 2024 ).

In South Korea, the Ministry of Food and Drug Safety (MFDS) has established standards for managing PSP, DSP, ASP, and tetrodotoxin (TTX) (Table  1 ). However, numerous other biotoxins remain unregulated. Meanwhile, the US Food and Drug Administration (FDA) and the European Food Safety Authority (EFSA) acknowledge the potential risks posed by a broader range of biotoxins and have established standards to ensure the safety of marine products. With global warming, toxic marine species from tropical and subtropical regions, including microalgae, fish, and benthic invertebrates, are migrating northward (Gobler et al. 2017 ). This trend necessitates more comprehensive toxin management due to the potential introduction of new toxins. The diversification of seafood import sources further underscores the need for enhanced biotoxin regulation. Consequently, in South Korea, the bioaccumulation of marine biotoxins and incidents of seafood poisoning have emerged as significant issues. Recent observations confirm the presence of more toxic marine organisms in Korean coastal waters, correlated with increasing seawater temperatures (Kim et al. 2023 ). Recent advancements in cutting-edge analytical instruments have enabled more sensitive and high-resolution quantitation of these marine biotoxins (Panda et al. 2022 ).

In response, the MFDS initiated the “Establishment of Safety Management System for Marine Biotoxins” R&D project (20163MFDS641) for 2020–2024, with a total budget of 16,793,000,000 KRW. This project involves 14 institutions and 124 researchers, focusing on:

Enhancing the management of currently regulated marine biotoxins;

Developing analytical methods and conducting surveys for unregulated toxins;

Developing rapid detection techniques for marine biotoxins;

Developing standard materials for marine biotoxins; and

Advancing marine biotoxin toxicity evaluation techniques.

This initiative aims to improve technology and understanding of marine biotoxin contamination characteristics and analytical methods in South Korea. Expected outcomes include establishing infrastructure for marine biotoxin analysis and training specialized personnel.

In this special issue of the Ocean Science Journal , we aim to disseminate accumulated knowledge and technological advancements related to marine biotoxins by Korean researchers for both domestic and international readers. The special issue comprises nine papers covering topics such as analytical methods for lipophilic biotoxins and cyclic imines, distribution characteristics of toxic microalgae along Korean coasts, bioaccumulation of TTX, PSP production mechanisms, separation for quantitative accuracy of palytoxin, and toxicity testing methods for biotoxins. These results will serve as baseline data for future research on marine biotoxin contamination, causative organisms, and newly introduced toxins in coastal waters and seafood in South Korea. We extend our gratitude to the editors and reviewers of the Ocean Science Journal for their support in publishing this special issue.

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Hong, S., Hong, HK. & Choi, KS. Safety Management of Marine Biotoxins in South Korea: Analytical Methods, Occurrence, and Risk Assessment. Ocean Sci. J. 59 , 41 (2024). https://doi.org/10.1007/s12601-024-00168-9

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ECDC launches e-learning course and operational tool for development of rapid risk assessments

ECDC launches a new e-learning course on rapid risk assessments (RRA) as well as an operational tool that describes the RRA methodology used by the Centre. The course and the tool support public health experts responsible for developing RRAs at European, national or sub-national level.

The operational tool facilitates the structured and reproducible development of RRAs for communicable diseases incidents. It is consistent with the ECDC operational guidance on RRAs that was published in 2011.

The operational tool highlights the key components of a good RRA and presents in detail each of the five stages involved in the development of an RRA:

• Define the risk questions;
• Collect and validate event information;
• Perform a literature search and extract evidence;
• Appraisal of evidence;
• Estimation of risk.

It also provides checklists to ensure that all the required information is being considered, as well as algorithms for assessing probability and impact.

The e-learning course addresses persons with no prior knowledge of working on RRAs, is open to all and available for free on ECDC's Virtual Academy (EVA). It enables participants to recognise the steps in RRA production, to describe what is expected to be done in each step and to contribute to a team working on RRAs. It is self-paced, with no specific starting date, and the estimated duration is between 3 and 5 hours of active learning. Those interested can log-in at the EVA platform for full access to the course.

As part of its core work of monitoring and assessing threats to public health in Europe from infectious diseases, RRAs are an integral part of ECDC’s work. RRAs are undertaken in the initial stages of an event or incident of potential public health concern, whereas more comprehensive risk assessments, which often include the conduct of full systematic reviews, are produced at a later stage of an event, usually when more time and information are available. The goal for ECDC is that an RRA should be produced within a limited timeframe.

Read more on the ECDC website

Surveillance and monitoring

Operational tool on rapid risk assessment methodology - ECDC 2019

Rapid risk assessments (RRA) are undertaken in the initial stages of an event or incident of potential public health concern while more comprehensive risk assessments, which often include the conduct of full systematic reviews, are produced at a later stage of an event, usually when more time and information are available.

E-learning course

Rapid Risk Assessment e-Learning course

This course is designed to target individuals with no prior knowledge of Rapid Risk Assessment (RRA). It may also be used as a refreshment module prior to advanced courses in Risk Assessment.

Methodology

Risk assessment

Operational guidance on rapid risk assessment methodology

This guidance document develops a methodology for rapid risk assessments undertaken in the initial stages of an event or incident of potential public health concern.