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Identifying regions most likely to contribute to an epidemic outbreak in a human mobility network
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-0322-8698
2021 (English)In: 2021 Swedish Artificial Intelligence Society Workshop (SAIS), IEEE, 2021, p. 1-4Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2021. p. 1-4
National Category
Public Health, Global Health, Social Medicine and Epidemiology Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-86672DOI: 10.1109/SAIS53221.2021.9483971ISI: 000855522600002Scopus ID: 2-s2.0-85111597313OAI: oai:DiVA.org:ltu-86672DiVA, id: diva2:1585337
Conference
33rd Workshop of the Swedish Artificial Intelligence Society (SAIS 2021), online, 14-15 June, 2021
Funder
European Regional Development Fund (ERDF)
Note

ISBN för värdpublikation: 978-1-6654-4236-7

Available from: 2021-08-17 Created: 2021-08-17 Last updated: 2023-09-04Bibliographically approved

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Bóta, András

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