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Analysis of the real number of infected people by COVID-19: A system dynamics approach

Author

Listed:
  • Bo Hu
  • Matthias Dehmer
  • Frank Emmert-Streib
  • Bo Zhang

Abstract

At the beginning of 2020, the COVID-19 pandemic was able to spread quickly in Wuhan and in the province of Hubei due to a lack of experience with this novel virus. Additionally, authories had no proven experience with applying insufficient medical, communication and crisis management tools. For a considerable period of time, the actual number of people infected was unknown. There were great uncertainties regarding the dynamics and spread of the Covid-19 virus infection. In this paper, we develop a system dynamics model for the three connected regions (Wuhan, Hubei excl. Wuhan, China excl. Hubei) to understand the infection and spread dynamics of the virus and provide a more accurate estimate of the number of infected people in Wuhan and discuss the necessity and effectivity of protective measures against this epidemic, such as the quarantines imposed throughout China. We use the statistics of confirmed cases of China excl. Hubei. Also the daily data on travel activity within China was utilized, in order to determine the actual numerical development of the infected people in Wuhan City and Hubei Province. We used a multivariate Monte Carlo optimization to parameterize the model to match the official statistics. In particular, we used the model to calculate the infections, which had already broken out, but were not diagnosed for various reasons.

Suggested Citation

  • Bo Hu & Matthias Dehmer & Frank Emmert-Streib & Bo Zhang, 2021. "Analysis of the real number of infected people by COVID-19: A system dynamics approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-9, March.
  • Handle: RePEc:plo:pone00:0245728
    DOI: 10.1371/journal.pone.0245728
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    References listed on IDEAS

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    1. Reuven Y. Rubinstein & Ruth Marcus, 1985. "Efficiency of Multivariate Control Variates in Monte Carlo Simulation," Operations Research, INFORMS, vol. 33(3), pages 661-677, June.
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