The first influenza pandemic in our century started in 2009, spreading from Mexico to the rest of the world, infecting a noticeable fraction of the world population. The outbreak reached Europe in late April, and eventually, almost all countries had confirmed H1N1 cases. On 6 May, Swedish authorities reported the first confirmed influenza case. By the time the pandemic ended, more than 10 thousand people were infected in the country. In this paper, we aim to discover critical socio-economic, travel, and environmental factors contributing to the spreading of H1N1 in Sweden covering six years between 2009 and 2015, focusing on 1. the onset and 2. the peak of the epidemic phase in each municipality. We apply the Generalized Inverse Infection Method (GIIM) to identify these factors. GIIM represents an epidemic spreading process on a network of nodes corresponding to geographical objects, connected by links indicating travel routes, and transmission probabilities assigned to the links guiding the infection process. The GIIM method uses observations on a real-life outbreak as a training dataset to estimate these probabilities and construct a simulated outbreak matching the training data as close as possible. Our results show that the influenza outbreaks considered in this study are mainly driven by the largest population centers in the country. Also, changes in temperature have a noticeable effect. Other socio-economic factors contribute only moderately to the epidemic peak and have a negligible effect on the epidemic onset. We also demonstrate that by training our model on the 2009 outbreak, we can predict the timing of the epidemic onset in the following five seasons with good accuracy. The model proposed in this paper provides a real-time decision support tool advising on resource allocation and surveillance. Furthermore, while this study only considers H1N1 outbreaks, the model can be adapted to other influenza strains or diseases with a similar transmission mechanism.
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