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Social simulations for intelligently beating COVID-19
Umeå University, Faculty of Science and Technology, Department of Computing Science.
The University of Edinburgh, UK.
The University of Caen, France.ORCID iD: 0000-0002-4147-4558
Stockholm University, Sweden.
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2020 (English)In: AI for Social Good workshop: accepted papers, 2020Conference paper, Published paper (Refereed)
Abstract [en]

The COVID-19 virus has led to a world-wide crisis that requires governments and stakeholders to take far-reaching decisions with limited knowledge of their consequences. This paper presents the AS- SOCC model as a valuable decision-support tool for anticipating the consequences of possible measures by considering many interwoven aspects at the individual, group and societal level. Moreover, this paper illustrates how this model can be applied to study the effects of different testing strategies on the spread of the virus and the healthcare system. We found that excluding age groups from random testing was ineffective, while prioritizing test- ing healthcare and education workers was effective, in combination with isolating the household of an infected person.

Place, publisher, year, edition, pages
2020.
Keywords [en]
COVID-19, Agent-Based Simulation, Decision Support, Values, Needs
National Category
Artificial Intelligence
Identifiers
URN: urn:nbn:se:umu:diva-194081OAI: oai:DiVA.org:umu-194081DiVA, id: diva2:1936959
Conference
AI for Social Good Workshop, virtual, July 20-21, 2020
Available from: 2025-02-12 Created: 2025-02-12 Last updated: 2025-02-13Bibliographically approved

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Kammler, ChristianVanhée, LoïsDignum, FrankDignum, VirginiaJensen, MaartenLudescher, Luis GustavoMellema, RenéPastrav, CezaraSjöström, Tomas

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Kammler, ChristianVanhée, LoïsDignum, FrankDignum, VirginiaJensen, MaartenLudescher, Luis GustavoMellema, RenéPastrav, CezaraSjöström, Tomas
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Department of Computing Science
Artificial Intelligence

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