How simulation modelling can help reduce the impact of COVID-19
By Christine S. M. Currie, John W. Fowler, Kathy Kotiadis, Thomas Monks, Bhakti Stephan Onggo, Duncan A. Robertson, Antuela A. Tako. J. Simulation, 2020
370 citations recorded by the source corpus.
ABSTRACT Modelling has been used extensively by all national governments and the World Health Organisation in deciding on the best strategies to pursue in mitigating the effects of COVID-19. Principally these have been epidemiological models aimed at understanding the spread of the disease and the impacts of different interventions. But a global pandemic generates a large number of problems and questions, not just those related to disease transmission, and each requires a different model to find the best solution. In this article we identify challenges resulting from the COVID-19 pandemic and discuss how simulation modelling could help to support decision-makers in making the most informed decisions. Modellers should see the article as a call to arms and decision-makers as a guide to what support is available from the simulation community.
Subfields: Emergency Medical Services, Management Science and Operations Research, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/354b8992680df857d2433a30e40b8e4db62f1f62
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In Management Science and Operations Research
In Modeling and Simulation
Citations in the corpus
- Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic (2020, background): “…an unprecedented series of shocks caused by the COVID-19 virus outbreak and global pandemic, a new instigator of SC disruptions quite unlike any seen in recent times (Chesbrough 2020; Choi 2020; Currie et al. 2020; Ivanov 2020a; Ivanov and Dolgui 2020b; Ivanov and Das 2020; Sarkis et al. 2020).”
- Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, methodology): “For interested readers, [73] provides an overview of the use of various simulation models, including those based on system dynamics [84], agent based models, discrete event simulations, and hybrid simulations.”