Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action
Permanent link
https://hdl.handle.net/10037/19057Date
2020-03-31Type
Journal articleTidsskriftartikkel
Peer reviewed
Author
Squazzoni, Flaminio; Polhill, J. Gareth; Edmonds, Bruce; Ahrweiler, Petra; Antosz, Patrycja; Scholz, Geeske; Chappin, Émile; Borit, Melania; Verhagen, Harko; Giardini, Francesca; Gilbert, NigelAbstract
The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability
of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to
the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite
the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing
specific local conditions and the social reactions of individuals. While experts have built simulation models to
estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to
limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic
crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper
calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls
on stakeholders to improve the rapidity with which data from trusted sources are released to the community
(in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and
the social/economic value of research.
Publisher
SimSoc ConsortiumCitation
Squazzoni F, Polhill, Edmonds, Ahrweiler P, Antosz, Scholz, Chappin, Borit M, Verhagen H, Giardini, Gilbert N. Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action. JASSS : Journal of Artificial Societies and Social Simulation. 2020;23(2)Metadata
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