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Dynamic models for CoVID-19 and data analysis
  • +3
  • Nian Shao,
  • Min Zhong,
  • Yue Yan,
  • Hanshuang Pan,
  • Jin Cheng,
  • Wenbin Chen
Nian Shao
Fudan University - Handan Campus
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Min Zhong
Southeast University
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Yue Yan
Shanghai University of Finance and Economics
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Hanshuang Pan
Fudan University
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Jin Cheng
Fudan University
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Wenbin Chen
Fudan University
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Peer review status:ACCEPTED

02 Mar 2020Submitted to Mathematical Methods in the Applied Sciences
03 Mar 2020Assigned to Editor
03 Mar 2020Submission Checks Completed
03 Mar 2020Editorial Decision: Accept

Abstract

In this letter, two time delay dynamic models, TDD-NCP model and Fudan-CCDC model, are introduced to track the data of COVID-19. The TDD-NCP model is developed recently by Cheng's group group in Fudan and SUFE. The TDD-NCP model introduced the time delay process into the differential equations to describe the latent period of the epidemic. The Fudan-CDCC model is established when Wenbin Chen suggested to determine the kernel functions in the TDD-NCP model by the public data from CDCC. By the public data of the cumulative confirmed cases in different regions in China and different countries, these models can clearly illustrate that the containment of the epidemic highly depends on early and effective isolations.