Identifying regions most likely to contribute to an epidemic outbreak in 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.In this paper we present our work in developing a network-based infection model between the municipalities of Sweden in order to identify the areas most likely to contribute to an epidemic. We first construct a human mobility model based on the well-known radiation model, then we employ a network-based compartmental model to simulate epidemic outbreaks with various parameters. Finally, we adopt the influence maximization problem known in network science to identify the municipalities having the largest impact on the spreading of infectious diseases.We only present the first part of our work in this paper. In the future, we plan to investigate the robustness of our model in identifying high-risk areas by simulating outbreaks with various parameters. We also plan to extend our work to selecting the most likely infection paths contributing to the spreading of infectious diseases.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Machine Learning

Maskininlärning

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