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

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.

EpiMix: A novel method to estimate effective reproduction number

“…This approach shares similarities with EpiRegress and the method proposed by Flaxman et al. ( Flaxman et al., 2020 ; Jin et al., 2022 ). Periodicity was observed in estimates of in both the simulations and case studies, probably because mobility patterns, an important driver of the linear predictors ( Table 1 ), tended to have a weekly cycle.…”

“…In previous work, the authors developed EpiRegress to provide real-time estimation ( Jin et al., 2022 ), wherein the time-varying reproduction number is taken to be the product of the effects of a diverse set of exogenous factors, including but not limited to mobility patterns and non-pharmaceutical interventions, and is linked to daily case counts through a negative binomial relationship. EpiRegress showed that the introduction of external data can effectively reduce uncertainty in the estimation of in low-incidence scenarios.…”

The rapid and efficient strategy for SARS-CoV-2 Omicron transmission control: analysis of outbreaks at the city level

“…The R t of the model was quantified using the EpiNow2 package (https:// cran.r-proje ct. org/ web/ packa ges/ EpiNo w2) [30]. It estimates the time-varying reproduction number of infectors through the date of infection.…”

rtestim: Time-varying reproduction number estimation with trend filtering

“…Ho et al (2023) estimates R t while monitoring the time-varying level of overdispersion. There are other spline-based approaches such as Azmon et al (2014); Gressani et al (2022a), autoregressive models with random effects (Jin et al, 2023) that are robust to low incidence, and generalized autoregressive moving average (GARMA) models (Hettinger et al, 2023) that are robust to measurement errors in incidence data.…”