Modelling COVID-19 contagion: risk assessment and targeted mitigation policies
Rama Cont (),
Artur Kotlicki and
Renyuan Xu
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Rama Cont: LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique, MI - Mathematical Institute [Oxford] - University of Oxford
Renyuan Xu: MI - Mathematical Institute [Oxford] - University of Oxford
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Abstract:
We use a spatial epidemic model with demographic and geographic heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasises the role of variability of regional outcomes and hetero-geneity across age groups and geographic locations, and provides a framework for assessing the impact of policies targeted towards sub-populations or regions. We define a concept of efficiency for comparative analysis of epidemic control policies and show targeted mitigation policies based on local monitoring to be more efficient than country-level or non-targeted measures. In particular, our results emphasise the importance of shielding vulnerable subpopulations and show that targeted policies based on local monitoring can considerably lower fatality forecasts and, in many cases, prevent the emergence of second waves which may occur under centralised policies.
Keywords: metapopulation epidemic models; network model; compartmental models; SEIAR model; Nowcasting systems; Stochastic models; nowcasting 1; SARS-n-COV; COVID-19; nowcasting (search for similar items in EconPapers)
Date: 2020-12-09
New Economics Papers: this item is included in nep-net
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