• Bull. World Health Organ. · Apr 2012

    Epidemic and intervention modelling--a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic.

    • Maria D Van Kerkhove and Neil M Ferguson.
    • Imperial College London, MRC Centre for Outbreak Analysis and Modelling, W2 1PG London, England. m.vankerkhove@imperial.ac.uk
    • Bull. World Health Organ. 2012 Apr 1; 90 (4): 306-10.

    ProblemOutbreak 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.ApproachSoon 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.SettingThe 2009 A(H1N1) influenza pandemic.Relevant ChangesModellers 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 LearntA 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.

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