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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 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.

SUBMITTER: Jung SM

PROVIDER: S-EPMC8498007 | biostudies-literature |

REPOSITORIES: biostudies-literature

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