EpiMix: A novel method to estimate effective reproduction number

Shihui Jin; Borame Lee Dickens; Jue Tao Lim; Alex R Cook

doi:10.1016/j.idm.2023.06.002

EpiMix: A novel method to estimate effective reproduction number

Infect Dis Model. 2023 Jun 20;8(3):704-716. doi: 10.1016/j.idm.2023.06.002. eCollection 2023 Sep.

Authors

Shihui Jin¹, Borame Lee Dickens¹, Jue Tao Lim², Alex R Cook^{1

3}

Affiliations

¹ Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore.
² Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
³ Department of Statistics and Data Science, National University of Singapore, Singapore.

Abstract

Transmission potential of a pathogen, often quantified by the time-varying reproduction number $R_{t}$ , provides the current pace of infection for a disease and indicates whether an emerging epidemic is under control. In this study, we proposed a novel method, EpiMix, for $R_{t}$ estimation, wherein we incorporated the impacts of exogenous factors and random effects under a Bayesian regression framework. Using Integrated Nested Laplace Approximation, EpiMix is able to efficiently generate reliable, deterministic $R_{t}$ estimates. In the simulations and case studies performed, we further demonstrated the method's robustness in low-incidence scenarios, together with other merits, including its flexibility in selecting variables and tolerance of varying reporting rates. All these make EpiMix a potentially useful tool for real-time $R_{t}$ estimation provided that the serial interval distribution, time series of case counts and external influencing factors are available.

Keywords: Epidemics; INLA; Regression; Reproduction number; SARS-CoV-2; Transmission dynamics.