Abstract
OBJECTIVES: The effective reproduction number (R(t)) has been critical for assessing the effectiveness of countermeasures during the coronavirus disease 2019 (COVID-19) pandemic. Conventional methods using reported incidences are unable to provide timely R(t) data due to the delay from infection to reporting. Our study aimed to develop a framework for predicting R(t) in real time, using timely accessible data - i.e. human mobility, temperature, and risk awareness. METHODS: A linear regression model to predict R(t) was designed and embedded in the renewal process. Four prefectures of Japan with high incidences in the first wave were selected for model fitting and validation. Predictive performance was assessed by comparing the observed and predicted incidences using cross-validation, and by testing on a separate dataset in two other prefectures with distinct geographical settings from the four studied prefectures. RESULTS: The predicted mean values of R(t) and 95% uncertainty intervals followed the overall trends for incidence, while predictive performance was diminished when R(t) changed abruptly, potentially due to superspreading events or when stringent countermeasures were implemented. CONCLUSIONS: 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.