Epidemic And Intervention Modelling – A Scientific .

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Lessons from the fieldEpidemic and intervention modelling – a scientific rationale for policydecisions? Lessons from the 2009 influenza pandemicMaria D Van Kerkhovea & Neil M FergusonaProblem Outbreak analysis and mathematical modelling are crucial for planning public health responses to infectious disease outbreaks,epidemics and pandemics. This paper describes the data analysis and mathematical modelling undertaken during and following the 2009influenza pandemic, especially to inform public health planning and decision-making.Approach Soon after A(H1N1)pdm09 emerged in North America in 2009, the World Health Organization convened an informal mathematicalmodelling network of public health and academic experts and modelling groups. This network and other modelling groups worked withpolicy-makers to characterize the dynamics and impact of the pandemic and assess the effectiveness of interventions in different settings.Setting The 2009 A(H1N1) influenza pandemic.Relevant changes Modellers provided a quantitative framework for analysing surveillance data and for understanding the dynamics ofthe epidemic and the impact of interventions. However, what most often informed policy decisions on a day-to-day basis was arguablynot sophisticated simulation modelling, but rather, real-time statistical analyses based on mechanistic transmission models relying onavailable epidemiologic and virologic data.Lessons learnt A key lesson was that modelling cannot substitute for data; it can only make use of available data and highlight what additionaldata might best inform policy. Data gaps in 2009, especially from low-resource countries, made it difficult to evaluate severity, the effects ofseasonal variation on transmission and the effectiveness of non-pharmaceutical interventions. Better communication between modellers andpublic health practitioners is needed to manage expectations, facilitate data sharing and interpretation and reduce inconsistency in results.BackgroundOutbreak analysis and mathematical modelling have playedan important role in the planning of the public healthresponse to infectious disease outbreaks, epidemics andpandemics. These tools can help quantify the risk to human health posed by a new infectious organism, rapidlyanalyse and interpret limited data in the early stages of anepidemic, and use such analysis to predict future developments. All of these actions are necessary to evaluate thepotential benefits of specific control measures. Statisticaland mathematical models integrate and synthesize epidemiological, clinical, virologic, genetic and sociodemographic data to gain quantitative insights into patterns ofdisease transmission. 1Soon after the emergence of A(H1N1)pdm09 in NorthAmerica in 2009, the World Health Organization (WHO)convened an informal mathematical modelling network ofpublic health experts and mathematical modelling groups inacademic institutions. This network worked collaboratively tocharacterize the dynamics and impact of the pandemic anddemonstrate the potential outcome of various interventions indifferent settings. This work was published in formats suitablefor various audiences, including technical experts, policymakers and the general public. Emphasis was on adaptingand interpreting experiences from developed countries forapplication to low-resource settings.2In this paper we provide an overview of the analysis andmathematical modelling undertaken during and following the2009 pandemic, with an emphasis on research of relevance topublic health planning and decision-making.Pre-pandemic planningMathematical models have been used by ministries of healthand governments to inform influenza pandemic planning inmany developed countries. Planning assumptions – in whichdisease severity (e.g. the case-fatality ratio) and the transmission characteristics (e.g. the basic reproductive number, R0)of the influenza virus are based on past pandemics (e.g. 1918,1957, 1968) or potential pandemic viral strains (e.g. highlypathogenic avian influenza subtype H5N1) – are modelledto estimate the potential incidence trajectory of infected andfatal cases and the likely impact of control measures. Suchinformation makes it possible to determine the medical andnon-medical interventions required, the feasibility of containment and the optimal size of the medication stockpile and bestuse of pharmaceuticals once a pandemic begins.3,4Modelling during the 2009 pandemicDuring the 2009 A(H1N1) pandemic, members of the influenza modelling community worked closely with public healthagencies and ministries of health. Efforts focused on rapidlyquantifying transmission to provide evidence for WHO pandemic phase changes;5 assessing severity6 and seasonality;7,8interpreting epidemiologic trends over time; measuring antigenic changes in the virus9 and assessing the potential impactof interventions.10,11 Modellers in public health agencies alsoprovided input into study design and helped to identify keydata to address public health challenges.12,13Although mathematical modelling was used for planningpurposes and to explore mitigation options in many countriesof the Americas (e.g. Canada, Mexico and the United States ofAmerica), Europe (e.g. France, Germany, the Netherlands andthe United Kingdom of Great Britain and Northern Ireland),Imperial College London, MRC Centre for Outbreak Analysis and Modelling, W2 1PG London, England.Correspondence to Maria D Van Kerkhove (e-mail: m.vankerkhove@imperial.ac.uk).(Submitted: 20 October 2011 – Revised version received: 17 February 2012 – Accepted: 22 February 2012 )a306Bull World Health Organ 2012;90:306–310 doi:10.2471/BLT.11.097949

Lessons from the fieldEpidemic and intervention modellingMaria D Van Kerkhove & Neil M FergusonAsia (e.g. China and Japan) and thePacific (Australia and New Zealand), itwas not sophisticated simulation modelling, but rather, real-time statisticalanalyses based on mechanistic transmission models and the interpretation ofemerging epidemiologic and virologicdata that most often informed policydecisions on a day-to-day basis. Theseresults were widely disseminated inpeer-reviewed publications, yet muchof the advice and guidance derivedfrom the modelling was never formallypublished but was presented insteadduring face-to-face meetings with national policy-makers, with occasionaldocumentation in meeting minutes orreports.Early outbreak investigations provided data that proved critical for characterizing the epidemiology of infection withA(H1N1)pdm09 in communities, schoolsand households. They made it possibleto estimate R0, serial intervals and agespecific clinical attack rates and to trackthe temporal distribution of secondaryinfections.5 These parameters were essential in assessing the burden of infectionwith A(H1N1)pdm09 and disease severity. Early rapid analyses with limited dataperformed to inform policy decisions werethen followed by more detailed studies thatmade use of more reliable and completedata. For example, retrospective analysesof publicly available epidemiologic andvirologic data from several countriesprovided a unique opportunity to comparethe spread of the same virus in differentcountries and to determine if differencesin latitude, temperature, humidity, population age structure or mixing patternsaffected transmission dynamics.14Policy decisions about the optimaluse, effectiveness and cost-effectivenessof pharmaceutical (e.g. antivirals orvaccines) and non-pharmaceuticalinterventions (e.g. school closures, social distancing measures, masks) wereheavily influenced by the results ofmathematical modelling.11,15 Since antivirals and vaccines were in short supplyor unavailable in many countries at thestart of the 2009 pandemic (and, in somecountries, throughout the pandemic),modelling provided guidance for the optimal use of such interventions to reducetransmission by targeting school-agedchildren and other high transmitters,or to reduce morbidity and mortalityby targeting high-risk individuals, suchas those with chronic underlying conditions or pregnant women.School closure was a policy optionconsidered in some countries. AlthoughA(H1N1)pdm09 caused milder diseasethan initially expected, some countries,such as Argentina and Japan, closed allschools early in their epidemic by extension of or overlap with school holidays,while others closed only certain schools.Modelling proved useful in weighing thepotential health benefits of school closures against their social and economiccosts. During the pandemic, modellinggroups in several countries, includingAustralia, China (Hong Kong SpecialAdministrative Region), France, Japan,the Netherlands, the United Kingdomand the United States, confidentiallyshared unpublished results with WHOand other WHO Members States via theWHO mathematical modelling networkto inform decision-making.2Lessons and challengesIt is difficult to reliably assess the extentto which modelling informed decisionmaking during the 2009 pandemic. Thisis because modellers and biostatisticiansin most countries provided advice as partof highly interactive multidisciplinaryadvisory groups, whose contributionsoften consisted of presenting formalmodelling results and a mechanisticdynamic perspective on the unfoldingepidemic. Furthermore, policy-makersneeded to weigh not only the potentialhealth benefits of different interventions,but also the economic, social, politicaland ethical costs associated with particular policy options. What is certain,however, is that the insights gained fromstatistical modelling informed policy inmany countries.Despite good achievements, severalchallenges remain. To set realistic expectations, improved communicationbetween policy-makers and the publicabout what modelling can and cannotdeliver is essential. It is also important toeffectively communicate how predictiondiffers from scenario modelling. Scenarios are useful in planning for assessingthe effectiveness of interventions andvarious policy options, but they are notpredictions. The failure to communicateuncertainty was problematic and led tomisunderstanding of modelling resultsduring the 2009 pandemic.Political pressures during the 2009pandemic were intense. The data available often failed to match the information needs of policy-makers. KeyBull World Health Organ 2012;90:306–310 doi:10.2471/BLT.11.097949decisions, such as how much vaccineto purchase, had to be made despitegreat uncertainty surrounding the likelyoverall health impact of the pandemic.Analyses conducted in “real time” using limited data are always subject tosubstantial uncertainty, and centralestimates and worst-case assessmentsare invariably subject to change as moredata become available.As expected, fundamental datagaps early in the pandemic, especiallyon population infection rates over time,made it very difficult to accurately assessits impact and disease severity. Manycountries had reliable and timely dataon the demand for primary health caredue to influenza-like illness but verylimited data on the proportion of individuals who were becoming infectedand seeking health care. As a result, thenumbers of symptomatic cases whowere seeking medical care could not beused to estimate the overall incidence ofinfluenza infection in the community.Real-time serosurveillance data couldhave filled this gap, but such data werenot available in any country beforethe first peak of pandemic influenzaactivity.13 Other data gaps also made itdifficult to evaluate the likely impact ofseasonal variation on transmission7,8 orthe effectiveness of many non-pharmaceutical interventions, particularly inlow-resource settings.Several important lessons werelearnt from the 2009 pandemic (Box 1).Chief among them is that modelling isnot a substitute for data. Rather, modelling provides a means for makingoptimal use of the data available and fordetermining the type of additional information needed to address policy-relevant questions. We must not, however,take too negative a view of achievementsin 2009. Modellers provided a quantitative framework for analysing surveillance data and for understanding boththe dynamics of the pandemic and theimpact of the interventions. Arguably, itwas such timely yet straightforward dataanalysis and interpretation that mostinformed the policy decisions madeduring the first months of the pandemic,rather than sophisticated pandemicsimulation modelling of the type usedfor pre-pandemic planning.In future, better coordination willbe needed not only among modellersand modelling groups, but also withclinicians, epidemiologists, virologistsand public health decision-makers. It307

Lessons from the fieldMaria D Van Kerkhove & Neil M FergusonEpidemic and intervention modellingBox 1. Summary of main lessons learnt Better serosurveillance and monitoring of community illness attack rates could have filleddata gaps (e.g. not knowing the underlying infection attack rate over time) that made itdifficult to estimate disease severity and to predict peak pandemic activity. Sharing and analysis of detailed epidemiologic data during the pandemic was crucial forinforming decisions, but data from low-resource countries was limited. Communication between modelling groups and policy-makers was good in severalcountries but could be improved further.will also be important to reduce inconsistencies and build consensus acrossmodelling groups. These goals willbe facilitated by the establishment ofnational and international modellingnetworks such as those that were createdin 2009. ملخص نمذجة الوباء والتدخل – أساس منطقي علمي للقرارات املتعلقة بالسياسات؟ الدروس املستفادة من جائحة أنفلونزا عام 2009 غري . بيانات الرصد ولفهم ديناميكيات الوباء وتأثري التدخالت أنه عىل نحو مثري للجدل مل يكن ما تستنري به القرارات املتعلقة بالسياسات يف الغالب عىل أساس يومي هو نمذجة املحاكاة بل كان التحليالت اإلحصائية البسيطة يف الوقت احلقيقي ، املتطورة القائمة عىل نامذج االنتقال امليكانيكي باالعتامد عىل بيانات األوبئة . والفريوسات املتاحة الدروس املستفادة أحد الدروس الرئيسية هو أن النمذجة ال ً يمكن أن تكون بدي ال عن البيانات؛ بل يمكنها فقط االستفادة من البيانات املتاحة والتأكيد عىل ما يمكن أن تستنري به السياسة من لقد جعلت فجوات البيانات . بيانات إضافية عىل النحو األمثل وباألخص الواردة من البلدان منخفضة املوارد من ،2009 يف الصعب تقييم الشدة وتأثريات االختالف املوسمي عىل االنتقال وثمة حاجة لتحسني االتصال . وفعالية التدخالت غري الصيدالنية بني واضعي النامذج ومماريس الصحة العمومية بغية إدارة التوقعات . وتسهيل مشاركة البيانات وتفسريها وتقليل التضارب يف النتائج املشكلة يعترب حتليل الفاشيات والنمذجة الرياضية يف غاية األمهية لتخطيط االستجابات الصحية العمومية لفاشيات األمراض وتصف هذه الورقة حتليل البيانات . املعدية واألوبئة واجلوائح والنامذج الرياضية التي تم إجراؤها خالل جائحة أنفلونزا عام وباألخص لتوفري معلومات لتخطيط الصحة ، وبعدها 2009. العمومية وصنع القرار A(H1N1)pdm09 األسلوب ُبعيد ظهور الفريوس الوبائي دعت منظمة الصحة العاملية لعقد ،2009 يف أمريكا الشاملية يف اجتامع لشبكة غري رسمية للنمذجة الرياضية من خرباء الصحة وعملت هذه . العمومية واخلرباء األكاديميني وجمموعات النمذجة الشبكة وجمموعات النمذجة األخرى مع صناع السياسة لوصف الديناميكيات وتأثري اجلائحة وتقييم فعالية التدخالت يف املواقع . املختلفة .2009 عام A(H1N1) املواقع املحلية جائحة اإلنفلونزا إطارا كم ًيا لتحليل التغيات ذات الصلة قدَّ م واضعو النامذج ًّ ر 摘要流行病和干预建模 – 政策决策的基本科学原理?2009 年流感大流行的经验教训问题 �重要。本文介绍 2009 ��的数据分析和数学建模。方法 2009 年北美出现 A(H1N1)pdm09 ��。当地状况 2009 A(H1N1) 流感大流行。相关变化 �型的简单、实?的统?分析。经验教训 09 ��RésuméÉpidémie et modélisation d’intervention - une justification scientifique aux décisions politiques? Leçons tirées de la pandémiede grippe de 2009Problème L’analyse de l’apparition d’une pandémie et sa modélisationmathématique sont cruciales pour la planification des réponses de santépublique à l’apparition de maladies infectieuses, d’épidémies et depandémies. Ce document décrit l’analyse de données et la modélisationmathématique entreprises pendant et après la pandémie de grippe de2009, en particulier pour orienter la planification des interventions desanté publique et la prise de décision.308Approche Peu après l’apparition du virus pandémique A(H1N1)pdm09en Amérique du Nord, en 2009, l’Organisation mondiale de la Santé arassemblé un réseau informel de modélisation mathématique composéd’experts de la santé publique, de spécialistes universitaires et des groupesde modélisation. Ce réseau et d’autres groupes de modélisation ont travailléavec les décideurs pour définir la dynamique et l’impact de la pandémie, etévaluer l’efficacité des interventions dans divers environnements.Bull World Health Organ 2012;90:306–310 doi:10.2471/BLT.11.097949

Lessons from the fieldMaria D Van Kerkhove & Neil M FergusonEnvironnement local La pandémie de grippe A(H1N1) de 2009.Changements significatifs Les modélisateurs ont fourni uncadre quantitatif pour l’analyse des données de surveillance et lacompréhension de la dynamique de l’épidémie et de l’impact desinterventions. Toutefois, au quotidien, les décisions politiques étaientsans doute plus souvent inspirées par des analyses statistiques simples,en temps réel, basées sur des modèles de transmission mécanistes etles données épidémiologiques et virologiques disponibles, que par unmodèle de simulation sophistiqué.Leçons tirées Un des enseignements principaux est que la modélisationEpidemic and intervention modellingne peut pas remplacer les données. Elle ne fait qu’utiliser les donnéesdisponibles et mettre en évidence les données supplémentairespouvant mieux éclairer les politiques. Le manque de données en 2009,en particulier en provenance des pays à faibles ressources, ont rendudifficile l’évaluation de la gravité, les effets des variations saisonnières surla transmission et l’efficacité des interventions non pharmaceutiques.Une meilleure communication entre les modélisateurs et les praticiensde la santé publique est nécessaire pour gérer les attentes, faciliterle partage et l’interprétation de données, et réduire les incohérencesentre les résultats.РезюмеМоделирование эпидемий и проведения мероприятий – научное обоснование для принятия решенийв отношении проводимых политик? Уроки, извлеченные из пандемии гриппа 2009 годаПроблема Анализ и математическое моделирование вспышекзаболеваний играют важную роль в планировании ответныхмер органов здравоохранения на вспышки инфекционныхзаболеваний, эпидемии и пандемии. В этом документеописывается анализ данных и математическое �ые во время и после пандемии гриппа в 2009 году.Основной целью этих мероприятий было предоставлениенеобходимой информации для осуществления планирования ипринятия решений органами здравоохранения.Подход Вскоре после пандемии вируса гриппа A(H1N1)pdm09в Северной Америке в 2009 году Всемирная я создала неформальную сеть ния из групп академических экспертов испециалистов по моделированию в сфере здравоохранения.Эта сеть и другие группы по моделированию сотрудничали ссоставителями политик с целью определения характеристик,динамики и влияния пандемий, а также оценки эффективностимероприятий в различных условиях.Местные условия Пандемия гриппа A(H1N1) в 2009 году.Осуществленные перемены Составители моделейпредоставили количественную основу для анализа данных поэпиднадзору, а также для понимания динамики распространенияэпидемий и влияния осуществленных мероприятий. Тем не менее,основная информация для принимающих решения органовпоступала на ежедневной основе не по результатам сложногоимитационного моделирования, но из простого и проводимогов реальном времени статистического анализа, основывающегосяна механистиче

modelling network of public health and academic experts and modelling groups. This network and other modelling groups worked with policy-makers to characterize the dynamics and impact of the pandemic and assess the effectiveness of interventions in different settings. Setting The 2009 A(H1N1) influenza pandemic.

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