Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling

Abstract

Influenza pandemics in the last century were characterized by successive waves and differences in impact and timing between different regions, for reasons not clearly understood. The 2009 H1N1 pandemic showed rapid global spread, but with substantial heterogeneity in timing within each hemisphere. Even within Europe substantial variation was observed, with the UK being unique in experiencing a major first wave of transmission in early summer and all other countries having a single major epidemic in the autumn/winter, with a West to East pattern of spread. Here we show that a microsimulation model, parameterised using data about H1N1pdm collected by the beginning of June 2009, explains the occurrence of two waves in UK and a single wave in the rest of Europe as a consequence of timing of H1N1pdm spread, fluxes of travels from US and Mexico, and timing of school vacations. The model provides a description of pandemic spread through Europe, depending on intra-European mobility patterns and socio-demographic structure of the European populations, which is in broad agreement with observed timing of the pandemic in different countries. Attack rates are predicted to depend on the socio-demographic structure, with age dependent attack rates broadly agreeing with available serological data. Results suggest that the observed heterogeneity can be partly explained by the between country differences in Europe: marked differences in school calendars, mobility patterns and sociodemographic structures. Moreover, higher susceptibility of children to infection played a key role in determining the epidemiology of the 2009 pandemic. Our work shows that it would have been possible to obtain a broad-brush prediction of timing of the European pandemic well before the autumn of 2009, much more difficult to achieve with simpler models or pre-pandemic parameterisation. This supports the use of models accounting for the structure of complex modern societies for giving insight to policy makers.

Modelling dynamical processes in complex socio-technical systems

“…An example is epidemic modelling, where spatially structured and agent-based models at various granularities (country, inter-city, intra-city) have been pushed to the computational limits with the integration of huge amount of data describing the flows of people and/or animals [93][94][95][96][97] . These models can generate results at an unprecedented level of detail and have been used successfully in the analysis and anticipation of real epidemics, such as the 2009 H1N1 pandemic 98,99 . Computer simulations thus become valuable in allowing both in silico experiments that would be infeasible in real systems and the capability to analyse and forecast scenarios.…”

Modeling and Predicting Human Infectious Diseases

“…Individuals are considered to be in contact with other members of their workplace during the day and with other household members during the night. In recent years, modelers have successfully expanded the large scale Agent Based approach to the country [6] and even continent level [56].…”

Epidemiological and statistical basis for detection and prediction of influenza epidemics

“…With these findings in mind, the main practical inference of this study is that further longitudinal research incorporating prospective evaluations of actionable alerts (Milinovich et al 2014) is required before eHealth data sources can be used as influenza monitoring tools in routine public health practice. Following this conclusion, a meta-narrative review was conducted in which two narratives for reporting prospective evaluation of influenza detection and prediction algorithms were identified.…”

“…The findings were promising and corresponded to the recent outcomes worldwide (Kim et al 2013, Timpka et al 2014a, Nagel et al 2013, Yom-Tov et al 2014, Kirian & Weintraub 2010, Socan et al 2012). However, it was concluded that further longitudinal research incorporating prospective evaluations of actionable alerts (Milinovich et al 2014) is required before eHealth surveillance systems can be used in routine public health practice. For that reason, in this thesis, both existing methods and the nowcasting method were applied to prospective syndromic data and naturally also to traditional influenza data.…”

“…The nowcasting method is, however, designed to only estimate the first peak, while the second peak is disregarded. This could possibly be handled by investigating indications of important societal changes, e.g., timing of school closures or vacations and fluxes of travels (Merler et al 2011), together with the use of the nowcasting method. If social distancing methods are applied in association with the arrival of a pandemic virus, there can be reasons to assume that epidemics may display two or more waves.…”