Abstract
Identifying the critical factors related to influenza spreading is crucial in predicting and mitigating epidemics. Specifically, uncovering the relationship between epidemic onset and various risk indicators such as socioeconomic, mobility and climate factors can reveal locations and travel patterns that play critical roles in furthering an outbreak. We study the 2009 A(H1N1) influenza outbreaks in Sweden’s municipalities between 2009 and 2015 and use the Generalized Inverse Infection Method (GIIM) to assess the most significant contributing risk factors. GIIM represents an epidemic spreading process on a network: nodes correspond to geographical objects, links indicate travel routes, and transmission probabilities assigned to the links guide the infection process. Our results reinforce existing observations that the influenza outbreaks considered in this study were driven by the country’s largest population centers, while meteorological factors also contributed significantly. Travel and other socioeconomic indicators have a negligible effect. We also demonstrate that by training our model on the 2009 outbreak, we can predict the epidemic onsets in the following five seasons with high accuracy.
Highlights
Identifying the critical factors related to influenza spreading is crucial in predicting and mitigating epidemics
We identified the week in which the epidemic onset happened in each municipality manually, by marking the week when the number of confirmed cases doubled compared to the previous week
We model the outbreak on a weekly basis and set up the input files of Generalized Inverse Infection Method (GIIM) as described in the Inputs section
Summary
Identifying the critical factors related to influenza spreading is crucial in predicting and mitigating epidemics. Uncovering the relationship between epidemic onset and various risk indicators such as socioeconomic, mobility and climate factors can reveal locations and travel patterns that play critical roles in furthering an outbreak. The mandatory reporting of the H1N1 pandemic strain continued in Sweden during the following five seasons, providing a detailed and thorough collection of epidemic data on a fine spatial resolution. With the increasing availability of geo-tagged epidemiological data, the 2009 A(H1N1) pandemic influenza has been studied using spatially explicit models. A previous geographic study of the pandemic spread over Sweden indicated a progression from the north to the south during 200912. Morris et al.[13] investigated the relationship between the spatio-temporal spreading of seasonal influenza and demographic, geographic and climatic factors. Apart from the importance of schoolchildren for influenza t ransmission[17], socioeconomic factors important for influenza spread remain sparsely s tudied[18]
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