Gostoli, U. and Silverman, E. (2023) Self-isolation and testing behaviour during the COVID-19 pandemic: an agent-based model. Artificial Life, 29(1), pp. 94-117. (doi: 10.1162/artl_a_00392) (PMID:36269874)
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Abstract
Since the beginning of the COVID-19 pandemic, various models of virus spread have been proposed. While most of these models focused on the replication of the interaction processes through which the virus is passed on from infected agents to susceptible ones, less effort has been devoted to the process through which agents modify their behaviour as they adapt to the risks posed by the pandemic. Understanding the way agents respond to COVID-19 spread is important, as this behavioural response affects the dynamics of virus spread by modifying interaction patterns. In this article, we present an agent-based model that includes a behavioural module determining agent testing and isolation propensity in order to understand the role of various behavioural parameters in the spread of COVID-19.
| Item Type: | Articles |
|---|---|
| Additional Information: | Umberto Gostoli and Eric Silverman are part of the Complexity in Health Improvement Programme supported by the Medical Research Council (MC_UU_00022/1) and the Chief Scientist Office (SPHSU16). This work was also supported by UK Prevention Research Partnership MR/S037594/1, which is funded by the the UK Research Councils, leading health charities, devolved administrations and the Department of Health and Social Care. |
| Keywords: | COVID-19, self-isolation, testing, agent-based modelling. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Silverman, Dr Eric and Gostoli, Dr Umberto |
| Authors: | Gostoli, U., and Silverman, E. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > MRC/CSO SPHSU |
| Journal Name: | Artificial Life |
| Publisher: | MIT Press |
| ISSN: | 1064-5462 |
| ISSN (Online): | 1530-9185 |
| Published Online: | 21 October 2022 |
| Copyright Holders: | Copyright © 2022 Massachusetts Institute of Technology |
| First Published: | First published in Artificial Life 29(1): 94-117 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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