Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves
File(s)
Published version
Author(s)
Parag, Kris V
Type
Journal Article
Abstract
We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.
Date Issued
2021-09-07
Date Acceptance
2021-08-13
Citation
PLoS Computational Biology, 2021, 17 (9), pp.1-23
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Start Page
1
End Page
23
Journal / Book Title
PLoS Computational Biology
Volume
17
Issue
9
Copyright Statement
© 2021 Kris V. Parag. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Sponsor
Medical Research Council (MRC)
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/34492011
PII: PCOMPBIOL-D-20-01808
Grant Number
MR/R015600/1
Subjects
01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
Bioinformatics
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2021-09-07