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Bull World Health Organ 2012;90:306–310 | doi:10.2471/BLT.11.097949
Lessons from the field
306
Epidemic and intervention modelling – a scientific rationale for policy
decisions? Lessons from the 2009 influenza pandemic
Maria D Van Kerkhovea & Neil M Fergusona
Background
Outbreak analysis and mathematical modelling have played
an important role in the planning of the public health
response to infectious disease outbreaks, epidemics and
pandemics. These tools can help quantify the risk to hu-
man health posed by a new infectious organism, rapidly
analyse and interpret limited data in the early stages of an
epidemic, and use such analysis to predict future develop-
ments. All of these actions are necessary to evaluate the
potential benefits of specific control measures. Statistical
and mathematical models integrate and synthesize epi-
demiological, clinical, virologic, genetic and sociodemo-
graphic data to gain quantitative insights into patterns of
disease transmission.
1
Soon aer the emergence of A(H1N1)pdm09 in North
America in 2009, the World Health Organization (WHO)
convened an informal mathematical modelling network of
public health experts and mathematical modelling groups in
academic institutions. is network worked collaboratively to
characterize the dynamics and impact of the pandemic and
demonstrate the potential outcome of various interventions in
dierent settings. is work was published in formats suitable
for various audiences, including technical experts, policy-
makers and the general public. Emphasis was on adapting
and interpreting experiences from developed countries for
application to low-resource settings.2
In this paper we provide an overview of the analysis and
mathematical modelling undertaken during and following the
2009 pandemic, with an emphasis on research of relevance to
public health planning and decision-making.
Pre-pandemic planning
Mathematical models have been used by ministries of health
and governments to inform inuenza pandemic planning in
many developed countries. Planning assumptions – in which
disease severity (e.g. the case-fatality ratio) and the transmis-
sion characteristics (e.g. the basic reproductive number, R0)
of the inuenza virus are based on past pandemics (e.g. 1918,
1957, 1968) or potential pandemic viral strains (e.g. highly
pathogenic avian inuenza subtype H5N1) – are modelled
to estimate the potential incidence trajectory of infected and
fatal cases and the likely impact of control measures. Such
information makes it possible to determine the medical and
non-medical interventions required, the feasibility of contain-
ment and the optimal size of the medication stockpile and best
use of pharmaceuticals once a pandemic begins.3,4
Modelling during the 2009 pandemic
During the 2009 A(H1N1) pandemic, members of the inu-
enza modelling community worked closely with public health
agencies and ministries of health. Eorts focused on rapidly
quantifying transmission to provide evidence for WHO pan-
demic phase changes;5 assessing severity6 and seasonality;7,8
interpreting epidemiologic trends over time; measuring anti-
genic changes in the virus9 and assessing the potential impact
of interventions.10,11 Modellers in public health agencies also
provided input into study design and helped to identify key
data to address public health challenges.12,13
Although mathematical modelling was used for planning
purposes and to explore mitigation options in many countries
of the Americas (e.g. Canada, Mexico and the United States of
America), Europe (e.g. France, Germany, the Netherlands and
the United Kingdom of Great Britain and Northern Ireland),
Problem 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 2009
influenza 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 mathematical
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.
Relevant changes Modellers provided a quantitative framework for analysing surveillance data and for understanding the dynamics of
the epidemic and the impact of interventions. However, what most often informed policy decisions on a day-to-day basis was arguably
not sophisticated simulation modelling, but rather, real-time statistical analyses based on mechanistic transmission models relying on
available 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 additional
data might best inform policy. Data gaps in 2009, especially from low-resource countries, made it difficult to evaluate severity, the effects of
seasonal variation on transmission and the effectiveness of non-pharmaceutical interventions. Better communication between modellers and
public health practitioners is needed to manage expectations, facilitate data sharing and interpretation and reduce inconsistency in results.
a 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 )
Bull World Health Organ 2012;90:306–310 | doi:10.2471/BLT.11.097949 307
Lessons from the field
Epidemic and intervention modelling
Maria D Van Kerkhove & Neil M Ferguson
Asia (e.g. China and Japan) and the
Pacic (Australia and New Zealand), it
was not sophisticated simulation mod-
elling, but rather, real-time statistical
analyses based on mechanistic transmis-
sion models and the interpretation of
emerging epidemiologic and virologic
data that most oen informed policy
decisions on a day-to-day basis. ese
results were widely disseminated in
peer-reviewed publications, yet much
of the advice and guidance derived
from the modelling was never formally
published but was presented instead
during face-to-face meetings with na-
tional policy-makers, with occasional
documentation in meeting minutes or
reports.
Early outbreak investigations pro-
vided data that proved critical for charac-
terizing the epidemiology of infection with
A(H1N1)pdm09 in communities, schools
and households. ey made it possible
to estimate R0, serial intervals and age-
specic clinical attack rates and to track
the temporal distribution of secondary
infections.5 ese parameters were es-
sential in assessing the burden of infection
with A(H1N1)pdm09 and disease sever-
ity. Early rapid analyses with limited data
performed to inform policy decisions were
then followed by more detailed studies that
made use of more reliable and complete
data. For example, retrospective analyses
of publicly available epidemiologic and
virologic data from several countries
provided a unique opportunity to compare
the spread of the same virus in dierent
countries and to determine if dierences
in latitude, temperature, humidity, popu-
lation age structure or mixing patterns
aected transmission dynamics.14
Policy decisions about the optimal
use, eectiveness and cost-eectiveness
of pharmaceutical (e.g. antivirals or
vaccines) and non-pharmaceutical
interventions (e.g. school closures, so-
cial distancing measures, masks) were
heavily inuenced by the results of
mathematical modelling.11,15 Since anti-
virals and vaccines were in short supply
or unavailable in many countries at the
start of the 2009 pandemic (and, in some
countries, throughout the pandemic),
modelling provided guidance for the op-
timal use of such interventions to reduce
transmission by targeting school-aged
children and other high transmitters,
or to reduce morbidity and mortality
by targeting high-risk individuals, such
as those with chronic underlying condi-
tions or pregnant women.
School closure was a policy option
considered in some countries. Although
A(H1N1)pdm09 caused milder disease
than initially expected, some countries,
such as Argentina and Japan, closed all
schools early in their epidemic by exten-
sion of or overlap with school holidays,
while others closed only certain schools.
Modelling proved useful in weighing the
potential health benets of school clo-
sures against their social and economic
costs. During the pandemic, modelling
groups in several countries, including
Australia, China (Hong Kong Special
Administrative Region), France, Japan,
the Netherlands, the United Kingdom
and the United States, condentially
shared unpublished results with WHO
and other WHO Members States via the
WHO mathematical modelling network
to inform decision-making.2
Lessons and challenges
It is dicult to reliably assess the extent
to which modelling informed decision-
making during the 2009 pandemic. is
is because modellers and biostatisticians
in most countries provided advice as part
of highly interactive multidisciplinary
advisory groups, whose contributions
oen consisted of presenting formal
modelling results and a mechanistic
dynamic perspective on the unfolding
epidemic. Furthermore, policy-makers
needed to weigh not only the potential
health benets of dierent interventions,
but also the economic, social, political
and ethical costs associated with par-
ticular policy options. What is certain,
however, is that the insights gained from
statistical modelling informed policy in
many countries.
Despite good achievements, several
challenges remain. To set realistic ex-
pectations, improved communication
between policy-makers and the public
about what modelling can and cannot
deliver is essential. It is also important to
eectively communicate how prediction
diers from scenario modelling. Scenar-
ios are useful in planning for assessing
the eectiveness of interventions and
various policy options, but they are not
predictions. e failure to communicate
uncertainty was problematic and led to
misunderstanding of modelling results
during the 2009 pandemic.
Political pressures during the 2009
pandemic were intense. e data avail-
able oen failed to match the infor-
mation needs of policy-makers. Key
decisions, such as how much vaccine
to purchase, had to be made despite
great uncertainty surrounding the likely
overall health impact of the pandemic.
Analyses conducted in “real time” us-
ing limited data are always subject to
substantial uncertainty, and central
estimates and worst-case assessments
are invariably subject to change as more
data become available.
As expected, fundamental data
gaps early in the pandemic, especially
on population infection rates over time,
made it very dicult to accurately assess
its impact and disease severity. Many
countries had reliable and timely data
on the demand for primary health care
due to inuenza-like illness but very
limited data on the proportion of in-
dividuals who were becoming infected
and seeking health care. As a result, the
numbers of symptomatic cases who
were seeking medical care could not be
used to estimate the overall incidence of
inuenza infection in the community.
Real-time serosurveillance data could
have lled this gap, but such data were
not available in any country before
the rst peak of pandemic inuenza
activity.13 Other data gaps also made it
dicult to evaluate the likely impact of
seasonal variation on transmission7,8 or
the eectiveness of many non-pharma-
ceutical interventions, particularly in
low-resource settings.
Several important lessons were
learnt from the 2009 pandemic (Box 1).
Chief among them is that modelling is
not a substitute for data. Rather, mod-
elling provides a means for making
optimal use of the data available and for
determining the type of additional in-
formation needed to address policy-rel-
evant questions. We must not, however,
take too negative a view of achievements
in 2009. Modellers provided a quantita-
tive framework for analysing surveil-
lance data and for understanding both
the dynamics of the pandemic and the
impact of the interventions. Arguably, it
was such timely yet straightforward data
analysis and interpretation that most
informed the policy decisions made
during the rst months of the pandemic,
rather than sophisticated pandemic
simulation modelling of the type used
for pre-pandemic planning.
In future, better coordination will
be needed not only among modellers
and modelling groups, but also with
clinicians, epidemiologists, virologists
and public health decision-makers. It
Bull World Health Organ 2012;90:306–310 | doi:10.2471/BLT.11.097949
308
Lessons from the field
Epidemic and intervention modelling Maria D Van Kerkhove & Neil M Ferguson
–
2009
2009
A(H1N1)pdm09
2009
2009A(H1N1)
2009
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 爆发分析和数?建模对规划传染病爆发、疾病流行和
大流行的公共卫生响?至关重要。本文介绍 2009 年流感
大流行期间及之后进行的特别用于为公共卫生计划和决策
制定提供情报的数据分析和数学建模。
方法 2009 年北美出现 A(H1N1)pdm09 流感病毒之后，
世界卫生组?随即召集公共卫生、学术专家和建模团队的
非正式数学建模网络。该网络和其他建模团?与决策者协
作描述流行病的动?和影响的特征，并评估不同环境中的
干预效果。
当地状况 2009 A(H1N1) 流感大流行。
相关变化 建模者提供了分析监测数据和理解流行病动态及
干预影响的定量分析框架。然而，日常最经常为?策提供情
报的无疑不是复杂的模拟建模，而是基于依赖可用流行病
学和病毒学?据的机械传播模型的简单、实?的统?分析。
经验教训 主要的经验教训是：建模替代不了数据；其只
能利用可用的数据以及突出可能对?策提供最重要信息的
补充数据。2009 年的数据缺口（尤其是财力不足的国家
造成的缺口）使评估严重性、季节性变量对?播的影响和
非药物干预效果非常困难。要管理预期、促进?据共享以
及解释并减少结果中的不一致，需要建模者和公共卫生参
与者之间更好的沟通。
Résumé
Épidémie et modélisation d’intervention - une justification scientifique aux décisions politiques? Leçons tirées de la pandémie
de grippe de 2009
Problème L’analyse de l’apparition d’une pandémie et sa modélisation
mathématique sont cruciales pour la planification des réponses de santé
publique à l’apparition de maladies infectieuses, d’épidémies et de
pandémies. Ce document décrit l’analyse de données et la modélisation
mathématique entreprises pendant et après la pandémie de grippe de
2009, en particulier pour orienter la planification des interventions de
santé publique et la prise de décision.
Approche Peu après l’apparition du virus pandémique A(H1N1)pdm09
en Amérique du Nord, en 2009, l’Organisation mondiale de la Santé a
rassemblé un réseau informel de modélisation mathématique composé
d’experts de la santé publique, de spécialistes universitaires et des groupes
de modélisation. Ce réseau et d’autres groupes de modélisation ont travaillé
avec les décideurs pour définir la dynamique et l’impact de la pandémie, et
évaluer l’efficacité des interventions dans divers environnements.
will also be important to reduce incon-
sistencies and build consensus across
modelling groups. ese goals will
be facilitated by the establishment of
national and international modelling
networks such as those that were created
in 2009. ■
Box 1. Summary of main lessons learnt
• Better serosurveillance and monitoring of community illness attack rates could have filled
data gaps (e.g. not knowing the underlying infection attack rate over time) that made it
difficult to estimate disease severity and to predict peak pandemic activity.
• Sharing and analysis of detailed epidemiologic data during the pandemic was crucial for
informing decisions, but data from low-resource countries was limited.
• Communication between modelling groups and policy-makers was good in several
countries but could be improved further.
Bull World Health Organ 2012;90:306–310 | doi:10.2471/BLT.11.097949 309
Lessons from the field
Epidemic and intervention modelling
Maria D Van Kerkhove & Neil M Ferguson
Environnement local La pandémie de grippe A(H1N1) de 2009.
Changements significatifs Les modélisateurs ont fourni un
cadre quantitatif pour l’analyse des données de surveillance et la
compréhension de la dynamique de l’épidémie et de l’impact des
interventions. Toutefois, au quotidien, les décisions politiques étaient
sans doute plus souvent inspirées par des analyses statistiques simples,
en temps réel, basées sur des modèles de transmission mécanistes et
les données épidémiologiques et virologiques disponibles, que par un
modèle de simulation sophistiqué.
Leçons tirées Un des enseignements principaux est que la modélisation
ne peut pas remplacer les données. Elle ne fait qu’utiliser les données
disponibles et mettre en évidence les données supplémentaires
pouvant mieux éclairer les politiques. Le manque de données en 2009,
en particulier en provenance des pays à faibles ressources, ont rendu
difficile l’évaluation de la gravité, les effets des variations saisonnières sur
la transmission et l’efficacité des interventions non pharmaceutiques.
Une meilleure communication entre les modélisateurs et les praticiens
de la santé publique est nécessaire pour gérer les attentes, faciliter
le partage et l’interprétation de données, et réduire les incohérences
entre les résultats.
Резюме
Моделирование эпидемий и проведения мероприятий – научное обоснование для принятия решений
в отношении проводимых политик? Уроки, извлеченные из пандемии гриппа 2009 года
Проблема Анализ и математическое моделирование вспышек
заболеваний играют важную роль в планировании ответных
мер органов здравоохранения на вспышки инфекционных
заболеваний, эпидемии и пандемии. В этом документе
описывается анализ данных и математическое моделирование,
осуществленные во время и после пандемии гриппа в 2009 году.
Основной целью этих мероприятий было предоставление
необходимой информации для осуществления планирования и
принятия решений органами здравоохранения.
Подход Вскоре после пандемии вируса гриппа A(H1N1)pdm09
в Северной Америке в 2009 году Всемирная организация
здравоохранения создала неформальную сеть математического
моделирования из групп академических экспертов и
специалистов по моделированию в сфере здравоохранения.
Эта сеть и другие группы по моделированию сотрудничали с
составителями политик с целью определения характеристик,
динамики и влияния пандемий, а также оценки эффективности
мероприятий в различных условиях.
Местные условия Пандемия гриппа A(H1N1) в 2009 году.
Осуществленные перемены Составители моделей
предоставили количественную основу для анализа данных по
эпиднадзору, а также для понимания динамики распространения
эпидемий и влияния осуществленных мероприятий. Тем не менее,
основная информация для принимающих решения органов
поступала на ежедневной основе не по результатам сложного
имитационного моделирования, но из простого и проводимого
в реальном времени статистического анализа, основывающегося
на механистических моделях передачи, использующих доступные
эпидемиологические и вирусологические данные.
Выводы Основной вывод заключается в том, что моделирование
не может заменить данные. Оно может только служить
инструментом для обработки доступных данных и указывать,
какие дополнительные сведения могут быть полезны при
составлении политик. Пробелы в данных 2009 года, особенно из
стран с ограниченными ресурсами, затруднили оценку тяжести
пандемии, последствий сезонных изменений при передаче
вируса и эффективность нефармацевтических мероприятий. Для
достижения ожидаемых результатов, стимулирования обмена
данными, а также для улучшения интерпретации результатов и
уменьшения количества несоответствий, необходимо повышение
качества обмена данными между составителями моделей и
сотрудниками здравоохранения.
Resumen
Modelización epidémica e intervencionista – ¿un fundamento científico para la toma de decisiones? Lecciones de la gripe
pandémica de 2009
Situación El análisis del brote y la modelización matemática son
cruciales para la planificación de respuestas de salud pública a los
brotes, epidémicos y pandémicos, de enfermedades infecciosas. Este
documento describe los análisis de datos y la modelización matemática
utilizados durante y después de la gripe pandémica de 2009. Su objetivo
principal era la obtención de información para la planificación y la toma
de decisiones en materia de salud pública.
Enfoque Poco después de que surgiera el virus pandémico A(H1N1)
pdm09 en Norteamérica en el año 2009, la Organización Mundial de la
Salud reunió una red informal de modelización matemática compuesta
por expertos académicos, expertos en salud pública y grupos de
modelización. Esta red y otros grupos de modelización trabajaron
con responsables políticos con el fin de caracterizar las dinámicas y
el impacto de la pandemia, así como para evaluar la eficacia de las
intervenciones en diversos escenarios.
Marco regional La gripe pandémica A(H1N1) de 2009.
Cambios importantes Los encargados de la modelización
proporcionaron un marco cuantitativo para analizar los datos de
vigilancia y para entender la dinámica de la epidemia y el impacto
de las intervenciones. No obstante, podría decirse que lo que con
mayor frecuencia informó a diario a las decisiones políticas no fue la
modelización de simulación sofisticada, sino simples análisis estadísticos
en tiempo real basados en los modelos mecanicistas de transmisión,
que se basan en los datos epidemiológicos y virológicos disponibles.
Lecciones aprendidas Una lección clave fue que la modelización
no puede sustituir a los datos, únicamente puede hacer uso de los
datos disponibles y destacar aquellos datos adicionales que puedan
ser la mejor información para la política. Las lagunas de datos en
2009, especialmente de los países con pocos recursos, dificultaron la
evaluación de la gravedad, los efectos de la variación estacional en
la transmisión y la eficacia de las intervenciones no farmacéuticas.
Es necesario mejorar la comunicación entre los encargados de la
modelización y los profesionales de salud pública para gestionar las
expectativas, facilitar que se compartan e interpreten datos y reducir
las incoherencias en los resultados.
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Lessons from the field
Epidemic and intervention modelling Maria D Van Kerkhove & Neil M Ferguson
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