Abstract

We use a spatial epidemic model with demographic and geographical heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasizes the role of variability of regional outcomes and heterogeneity across age groups and geographical locations, and provides a framework for assessing the impact of policies targeted towards subpopulations 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 emphasize 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 centralized policies.

Highlights

  • We use a spatial epidemic model with demographic and geographical heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England

  • We illustrate the usefulness of the framework by applying it to the study of COVID-19 dynamics across regions in England and showing how it may be used to reconstruct the latent progression of the epidemic and perform a comparative analysis of various mitigation policies through scenario projections

  • — Using a baseline epidemic model consistent with epidemiological data and observations on fatalities and cases reported in England up to June 2020, we estimate more than 17.8 million persons in England (31.7% of the population) to have been exposed to COVID-19 by 1 August 2020

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Summary

Overview

The novel coronavirus pandemic of 2019–2021 has led to disruption on a global scale, leading to more than 1.4 million deaths worldwide at the time of writing, and prompted the implementation of government policies involving a variety of ‘non-pharmaceutical interventions’ [1] including school closures, workplace restrictions, restrictions on social gatherings, social distancing and, in some cases, general lockdowns for extended periods. This has led to a range of different public health policies across the world, and the efficiency of specific policy choices has been subject to much debate. We first present below an overview of the main features of our approach and the key findings, before going into more detail on the methodology and results

Methodology
Summary of findings
Outline
Modelling framework
State variables
A metapopulation SEIAR model
Stochastic dynamics
Policies for epidemic control
Comparative analysis of mitigation policies
Data sources
Modelling of inter-regional mobility
Epidemiological parameters
Social contact rates
Incubation rate
Proportion of symptomatic and asymptomatic infections
Recovery rate γ
Infection fatality rates
Estimation of the infection rate
Inter-regional mobility and social contact during confinement
Goodness-of-fit
Observable quantities and uncertainty
Observable quantities
Implications of partial observability
Confinement followed by social distancing
Regional heterogeneity
Targeted policies
School closures
Shielding
Restrictions on social gatherings
Pubs and schools
Shielding of senior citizens
Uncertainty on the symptomatic ratio
Heterogeneity in infection rates
Adaptive mitigation policies
Centralized policies
Example
Impact of the triggering threshold Bon
Impact of demographic granularity
Decentralized policies: regional tier system
Adaptive versus pre-planned policies
Regional outcomes
Findings
Ferguson N et al 2020 Report 9
Full Text
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