Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves

• Main Author: Parag, K.V (Author)
• Format: Article
• Language: English
• Published: Public Library of Science 2021
• Subjects: algorithm; Algorithms; basic reproduction number; Basic Reproduction Number; Bayes theorem; Bayes Theorem; Bias; biology; communicable disease; communicable disease control; Communicable Disease Control; Communicable Diseases; Computational Biology; computer simulation; Computer Simulation; computer system; Computer Systems; COVID-19; epidemic; Epidemics; epidemiological monitoring; Epidemiological Monitoring; human; Humans; incidence; Incidence; influenza; Influenza A virus (H1N1); Influenza A Virus, H1N1 Subtype; Influenza, Human; Linear Models; Markov chain; Markov Chains; Models, Statistical; New Zealand; prevention and control; Retrospective Studies; retrospective study; SARS-CoV-2; statistical bias; statistical model; time factor; Time Factors; United States
• Online Access: View Fulltext in Publisher

 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. Copyright: © 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.