Dynamics of COVID-19 epidemics: SEIR models underestimate peak infection rates and overestimate epidemic duration

Grant, Alastair (2020) Dynamics of COVID-19 epidemics: SEIR models underestimate peak infection rates and overestimate epidemic duration. medRxiv. pp. 1-16.

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Abstract

Compartment models of infectious diseases, such as SEIR, are being used extensively to model the COVID-19 epidemic. Transitions between compartments are modelled either as instantaneous rates in differential equations, or as transition probabilities in discrete time difference or matrix equations. These models give accurate estimates of the position of equilibrium points, when the rate at which individuals enter each stage is equal to the rate at which they exit from it. However, they do not accurately capture the distribution of times that an individual spends in each compartment, so do not accurately capture the transient dynamics of epidemics. Here we show how matrix models can provide a straightforward route to accurately model stage durations, and thus correctly reproduce epidemic dynamics. We apply this approach to modelling the dynamics of a COVID-19 epidemic. We show that a SEIR model underestimates peak infection rates (by a factor of three using published parameter estimates based on the progress of the epidemic in Wuhan) and substantially overestimates epidemic persistence after the peak has passed.

Item Type: Article
Additional Information: This is now live on the medRxiv preprint server at some point today. It would be helpful to get the record sorted and ready to post, as it may attract quite a bit of interest.
Faculty \ School: Faculty of Science > School of Environmental Sciences
Depositing User: LivePure Connector
Date Deposited: 08 Apr 2020 00:46
Last Modified: 07 Jul 2020 23:55
URI: https://ueaeprints.uea.ac.uk/id/eprint/74729
DOI: 10.1101/2020.04.02.20050674

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