Identifying municipalities most likely to contribute to an epidemic outbreak in Sweden using a human mobility network

The importance of modelling the spreading of infectious diseases as part of a public health strategy has been highlighted by the ongoing coronavirus pandemic. This includes identifying the geographical areas or travel routes most likely to contribute to the spreading of an outbreak. These areas and routes can then be monitored as part of an early warning system, be part of intervention strategies, e.g. lockdowns, aiming to mitigate the spreading of the disease or be a focus of vaccination campaigns. This thesis focus on developing a network-based infection model between the municipalities of Sweden in order to identify the areas most likely to contribute to an epidemic. First, a human mobility model is constructed based on the well-known radiation model. Then a network-based SEIR compartmental model is employed to simulate epidemic outbreaks with various parameters. Finally, the adoption of the influence maximization problem known in network science to identify the municipalities having the largest impact on the spreading of infectious diseases. The resulting super-spreading municipalities point towards confirmation of the known fact that central highly populated regions in highly populated areas carry a greater risk than their neighbours initially. However, once these areas are targeted, the other resulting nodes show a greater variety in geographical location than expected. Furthermore, a correlation can be seen between increased infections time and greater variety, although more empirical data is required to support this claim. For further evaluation of the model, the mobility network was studied due to its central role in creating data for the model parameters. Commuting data in the Gothenburg region were compared to the estimations, showing an overall good accuracy with major deviations in few cases.