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Phenomenological and mechanistic models for predicting early transmission data of COVID-19.

Authors
  • Miyama, Takeshi1, 2
  • Jung, Sung-Mok2, 3
  • Hayashi, Katsuma2
  • Anzai, Asami2
  • Kinoshita, Ryo2
  • Kobayashi, Tetsuro2
  • Linton, Natalie M2, 3
  • Suzuki, Ayako2
  • Yang, Yichi3
  • Yuan, Baoyin3
  • Kayano, Taishi2
  • Akhmetzhanov, Andrei R3
  • Nishiura, Hiroshi2
  • 1 Division of Public Health, Osaka Institute of Public Health, Osaka, Japan. , (Japan)
  • 2 School of Public Health, Kyoto University, Kyoto, Japan. , (Japan)
  • 3 Graduate School of Medicine, Hokkaido University, Sapporo, Japan. , (Japan)
Type
Published Article
Journal
Mathematical biosciences and engineering : MBE
Publication Date
Jan 01, 2022
Volume
19
Issue
2
Pages
2043–2055
Identifiers
DOI: 10.3934/mbe.2022096
PMID: 35135241
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Forecasting future epidemics helps inform policy decisions regarding interventions. During the early coronavirus disease 2019 epidemic period in January-February 2020, limited information was available, and it was too challenging to build detailed mechanistic models reflecting population behavior. This study compared the performance of phenomenological and mechanistic models for forecasting epidemics. For the former, we employed the Richards model and the approximate solution of the susceptible-infected-recovered (SIR) model. For the latter, we examined the exponential growth (with lockdown) model and SIR model with lockdown. The phenomenological models yielded higher root mean square error (RMSE) values than the mechanistic models. When using the numbers from reported data for February 1 and 5, the Richards model had the highest RMSE, whereas when using the February 9 data, the SIR approximation model was the highest. The exponential model with a lockdown effect had the lowest RMSE, except when using the February 9 data. Once interventions or other factors that influence transmission patterns are identified, they should be additionally taken into account to improve forecasting.

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