Modelling COVID-19 contagion: risk assessment and targeted mitigation policies

Rama Cont (1, 2) , Artur Kotlicki (2) , Renyuan Xu (2)

1 LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation
2 MI - Mathematical Institute [Oxford]

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.

Mots clés: metapopulation epidemic models; network model; compartmental models; SEIAR model; Nowcasting systems; Stochastic models; nowcasting 1; SARS-n-COV; COVID-19; nowcasting

Domaines: Environnement et Société; Santé publique et épidémiologie; Gestion des risques [q-fin.RM]

Rama Cont, Artur Kotlicki, Renyuan Xu. Modelling COVID-19 contagion: risk assessment and targeted mitigation policies. 2020. ⟨hal-02923033v3⟩