Modelling COVID-19 Contagion: Risk Assessment and Targeted Mitigation Policies

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 heterogeneity 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 sub-populations 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.

A two diffusion stochastic model for the spread of the new corona virus SARS-CoV-2

“…It is sensible to assume that these parameters depend on the age and location of individuals and therefore they could be treated as functions of these two variables, allowing the application of this model to some age and/or location restricted subpopulation, for example (for similar approach see [28] ).…”

“…[25] ). For examples of stochastic models, quite different than the models in the present paper, we refer to the binomial distribution based discrete stochastic model in [26] , the stochastic version of the SEIR model in [27] and the refined SEIR model in [28] including stratification by age, mobility, social contacts and the impact of the asymptomatic cases. For another model incorporating spatial heterogeneity and stochasticity we refer to [29] .…”