Justified Stories with Agent-Based Modelling for Local COVID-19 Planning
Jennifer Badham (),
Pete Barbrook-Johnson (),
Camila Caiado () and
Brian Castellani ()
Additional contact information
Pete Barbrook-Johnson: https://www.cecan.ac.uk/about-us/cecan-team/pete-barbrook-johnson
Camila Caiado: https://www.dur.ac.uk/research/directory/staff/?id=5626
Brian Castellani: http://www.art-sciencefactory.com/briancastellani.html
Journal of Artificial Societies and Social Simulation, 2021, vol. 24, issue 1, 8
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.
Keywords: Agent-Based Modelling; Epidemic; COVID-19; Descriptive Model; Social Distancing; Justified Stories (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2020-138-3
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