Imperial College London
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DrSethFlaxman

Faculty of Natural Sciences, Department of Mathematics

Visiting Reader
 
 
 
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s.flaxman

 
 
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Location

 

522Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bhatt:2023:jrsssa/qnad030,
author = {Bhatt, S and Ferguson, N and Flaxman, S and Gandy, A and Mishra, S and Scott, JA},
doi = {jrsssa/qnad030},
journal = {Journal of the Royal Statistical Society Series A: Statistics in Society},
pages = {601--615},
title = {Semi-mechanistic Bayesian modelling of COVID-19 with renewal processes},
url = {http://dx.doi.org/10.1093/jrsssa/qnad030},
volume = {186},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title>               <jats:p>We propose a general Bayesian approach to modelling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular, an analysis concerning the effects of non-pharmaceutical interventions (NPIs) in reducing COVID-19 transmission in 11 European countries. The model parameterises the time-varying reproduction number Rt through a multilevel regression framework in which covariates can be governmental interventions, changes in mobility patterns, or other behavioural measures. Bayesian multilevel modelling allows a joint fit across regions, with partial pooling to share strength. This innovation was critical to our timely estimates of the impact of lockdown and other NPIs in the European epidemics: estimates from countries at later stages in their epidemics informed those of countries at earlier stages. Originally released as Imperial College Reports, the validity of this approach was borne out by the subsequent course of the epidemic. Our framework provides a fully generative model for latent infections and derived observations, including deaths, cases, hospitalizations, ICU admissions, and seroprevalence surveys. In this article, we additionally explore the confounded nature of NPIs and mobility. Versions of our model were used by New York State, Tennessee, and Scotland to estimate the current epidemic situation and make policy decisions.</jats:p>
AU  - Bhatt,S
AU  - Ferguson,N
AU  - Flaxman,S
AU  - Gandy,A
AU  - Mishra,S
AU  - Scott,JA
DO  - jrsssa/qnad030
EP  - 615
PY  - 2023///
SN  - 0964-1998
SP  - 601
TI  - Semi-mechanistic Bayesian modelling of COVID-19 with renewal processes
T2  - Journal of the Royal Statistical Society Series A: Statistics in Society
UR  - http://dx.doi.org/10.1093/jrsssa/qnad030
VL  - 186
ER  -
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