Predicting the effective reproduction number of COVID-19: Inference using human mobility, temperature, and risk awareness

Abstract

\n Objectives\n The effective reproduction number (\n \n R\n t\n \n ) is critical for assessing the effectiveness of countermeasures during the coronavirus disease 2019 (COVID-19) pandemic. Conventional methods using reported incidence are unable to provide \n \n R\n t\n \n timely due to the delay from infection to reporting. Here, we aim to develop a framework to predict the \n \n R\n t\n \n in real-time using timely accessible data, i.e., human mobility, temperature, and risk awareness.\n \n Methods\n A linear regression model to predict \n \n R\n t\n \n was designed and embedded in the renewal process. Four prefectures of Japan with high incidence in the first wave were selected for model fitting and validation. Predictive performance was assessed by comparing the observed and predicted incidence using cross-validation, by testing on a separate dataset in two other prefectures with distinct geographical settings from the four prefectures.\n \n Results\n The predicted mean values of \n \n R\n t\n \n and 95% uncertainty intervals well traced the overall trend of incidence, while predictive performance was diminished when \n \n R\n t\n \n abruptly changed potentially due to superspreading events and when stringent countermeasures were implemented.\n \n Conclusions\n The described model can potentially be used for monitoring the transmission dynamics of COVID-19 ahead of the formal estimates subject to delay, providing essential information for timely planning and assessment of countermeasures.\n