4.7 Article

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

Journal

INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
Volume 113, Issue -, Pages 47-54

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.ijid.2021.10.007

Keywords

COVID-19; effective reproduction number; regression model; mobility; temperature; Japan

Funding

  1. Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency of Japan [JPMEERF20S11804]
  2. Japan Society for the Promotion of Science (JSPS) KAKENHI [20J2135800]
  3. Nakajima Foundation
  4. Health and Labor Sciences Research Grants [19HA1003, 20CA2024, 20HA2007]
  5. Japan Agency for Medical Research and Development (AMED) [JP19fk0108104, JP20fk0108140, JP20fk0108535s0101]
  6. JSPS KAKENHI [17H04701, 21H03198]
  7. GAP fund program of Kyoto University
  8. Japan Science and Technology Agency (JST) CREST program [JPMJCR1413]
  9. SICORP program [JPMJSC20U3, JPMJSC2105]
  10. German Federal Ministry of Health (BMG)
  11. Grants-in-Aid for Scientific Research [21H03198, 17H04701] Funding Source: KAKEN

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This study aimed to develop a framework for predicting the effective reproduction number (Rt) of COVID-19 in real time using timely accessible data such as human mobility, temperature, and risk awareness. The results showed that predicted mean values of Rt and 95% uncertainty intervals followed overall trends for incidence, but predictive performance diminished when Rt changed abruptly.
Objectives: The effective reproduction number (Rt) 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 Rt data due to the delay from infection to reporting. Our study aimed to develop a framework for predicting Rt in real time, using timely accessible data - i.e. human mobility, temperature, and risk awareness. Methods: A linear regression model to predict Rt 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 val-idation. 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 Rt and 95% uncertainty intervals followed the overall trends for incidence, while predictive performance was diminished when Rt 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. (C) 2021 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.

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