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

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 pr

SUBMITTER: Jung SM 

PROVIDER: S-EPMC8498007 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature