Justified Stories with Agent-Based Modelling for Local COVID-19 Planning

This paper presents JuSt-Social, an agent-based model of the COVID-19 epidemic with a range of potential social policy interventions. It was developed to support local authorities in North East England who are making decisions in a fast moving crisis with limited access to data. The proximate purpos...

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Bibliographic Details
Published inJournal of artificial societies and social simulation Vol. 24; no. 1
Main Authors Badham, Jennifer, Barbrook-Johnson, Pete, Caiado, Camila, Castellani, Brian
Format Journal Article
LanguageEnglish
Published Guildford Department of Sociology, University of Surrey 2021
Subjects
Coronaviruses
COVID-19
Decision makers
Decision making
Disease transmission
Epidemics
Health education
Health problems
Immediate
Infectious diseases
Intervention
Local authorities
Policy making
Public health
Social policy
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Summary:This paper presents JuSt-Social, an agent-based model of the COVID-19 epidemic with a range of potential social policy interventions. It was developed to support local authorities in North East England who are making decisions in a fast moving crisis with limited access to data. The proximate purpose of JuSt-Social is description, as the model represents knowledge about both COVID-19 transmission and intervention effects. Its ultimate purpose is to generate stories that respond to the questions and concerns of local planners and policy makers and are justified by the quality of the representation. These justified stories organise the knowledge in way that is accessible, timely and useful at the local level, assisting the decision makers to better understand both their current situation and the plausible outcomes of policy alternatives. JuSt-Social and the concept of justified stories apply to the modelling of infectious disease in general and, even more broadly, modelling in public health, particularly for policy interventions in complex systems.
ISSN:1460-7425
1460-7425
DOI:10.18564/jasss.4532