In this section
@article{Mishra:2020:10.1101/2020.11.24.20236661,
author = {Mishra, S and Scott, J and Zhu, H and Ferguson, NM and Bhatt, S and Flaxman, S and Gandy, A},
doi = {10.1101/2020.11.24.20236661},
title = {A COVID-19 Model for Local Authorities of the United Kingdom},
url = {http://dx.doi.org/10.1101/2020.11.24.20236661},
year = {2020}
}
TY - JOUR
AB - <jats:title>Abstract</jats:title> <jats:p> We propose a new framework to model the COVID-19 epidemic of the United Kingdom at the level of local authorities. The model fits within a general framework for semi-mechanistic Bayesian models of the epidemic, with some important innovations: we model the proportion of infections that result in reported deaths and cases as random variables. This is in contrast to standard frameworks that model the latent infection as a deterministic function of time varying reproduction number, <jats:italic>R</jats:italic> <jats:sub> <jats:italic>t</jats:italic> </jats:sub> . The model is tailored and designed to be updated daily based on publicly available data. We envisage the model to be useful for now-casting and short-term projections of the epidemic as well as estimating historical trends. The model fits are available on a public website, <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://imperialcollegelondon.github.io/covid19local">https://imperialcollegelondon.github.io/covid19local</jats:ext-link> . The model is currently being used by the Scottish government in their decisions on interventions within Scotland [1, issue 24 to now]. </jats:p>
AU - Mishra,S
AU - Scott,J
AU - Zhu,H
AU - Ferguson,NM
AU - Bhatt,S
AU - Flaxman,S
AU - Gandy,A
DO - 10.1101/2020.11.24.20236661
PY - 2020///
TI - A COVID-19 Model for Local Authorities of the United Kingdom
UR - http://dx.doi.org/10.1101/2020.11.24.20236661
UR - https://doi.org/10.1101/2020.11.24.20236661
ER -
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