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Article

EpiRegress: A Method to Estimate and Predict the Time-Varying Effective Reproduction Number

1
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, # 10-01 Tahir Foundation Building, 12 Science Drive 2, Singapore 117549, Singapore
2
Department of Statistics and Data Science, National University of Singapore, Singapore 117546, Singapore
*
Author to whom correspondence should be addressed.
Viruses 2022, 14(7), 1576; https://doi.org/10.3390/v14071576
Submission received: 1 June 2022 / Revised: 19 July 2022 / Accepted: 19 July 2022 / Published: 20 July 2022
(This article belongs to the Special Issue Infectious Disease Epidemiology and Transmission Dynamics)

Abstract

The time-varying reproduction (Rt) provides a real-time estimate of pathogen transmissibility and may be influenced by exogenous factors such as mobility and mitigation measures which are not directly related to epidemiology parameters and observations. Meanwhile, evaluating the impacts of these factors is vital for policy makers to propose and adjust containment strategies. Here, we developed a Bayesian regression framework, EpiRegress, to provide Rt estimates and assess impacts of diverse factors on virus transmission, utilising daily case counts, mobility, and policy data. To demonstrate the method’s utility, we used simulations as well as data in four regions from the Western Pacific with periods of low COVID-19 incidence, namely: New South Wales, Australia; New Zealand; Singapore; and Taiwan, China. We found that imported cases had a limited contribution on the overall epidemic dynamics but may degrade the quality of the Rt estimate if not explicitly accounted for. We additionally demonstrated EpiRegress’s capability in nowcasting disease transmissibility before contemporaneous cases diagnosis. The approach was proved flexible enough to respond to periods of atypical local transmission during epidemic lulls and to periods of mass community transmission. Furthermore, in epidemics where travel restrictions are present, it is able to distinguish the influence of imported cases.
Keywords: Bayesian inference; COVID-19; epidemic control; regression; reproduction number Bayesian inference; COVID-19; epidemic control; regression; reproduction number

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MDPI and ACS Style

Jin, S.; Dickens, B.L.; Lim, J.T.; Cook, A.R. EpiRegress: A Method to Estimate and Predict the Time-Varying Effective Reproduction Number. Viruses 2022, 14, 1576. https://doi.org/10.3390/v14071576

AMA Style

Jin S, Dickens BL, Lim JT, Cook AR. EpiRegress: A Method to Estimate and Predict the Time-Varying Effective Reproduction Number. Viruses. 2022; 14(7):1576. https://doi.org/10.3390/v14071576

Chicago/Turabian Style

Jin, Shihui, Borame Lee Dickens, Jue Tao Lim, and Alex R. Cook. 2022. "EpiRegress: A Method to Estimate and Predict the Time-Varying Effective Reproduction Number" Viruses 14, no. 7: 1576. https://doi.org/10.3390/v14071576

APA Style

Jin, S., Dickens, B. L., Lim, J. T., & Cook, A. R. (2022). EpiRegress: A Method to Estimate and Predict the Time-Varying Effective Reproduction Number. Viruses, 14(7), 1576. https://doi.org/10.3390/v14071576

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