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A stochastic SEIHR model for COVID-19 data fluctuations.
Niu, Ruiwu; Chan, Yin-Chi; Wong, Eric W M; van Wyk, Michaël Antonie; Chen, Guanrong.
Afiliación
  • Niu R; College of Mathematics and Statistics, Shenzhen University, Shenzhen, 518060 People's Republic of China.
  • Chan YC; Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Ave., Kowloon Tong, Hong Kong.
  • Wong EWM; Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Ave., Kowloon Tong, Hong Kong.
  • van Wyk MA; School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, 2000 South Africa.
  • Chen G; Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Ave., Kowloon Tong, Hong Kong.
Nonlinear Dyn ; 106(2): 1311-1323, 2021.
Article en En | MEDLINE | ID: mdl-34248280
Although deterministic compartmental models are useful for predicting the general trend of a disease's spread, they are unable to describe the random daily fluctuations in the number of new infections and hospitalizations, which is crucial in determining the necessary healthcare capacity for a specified level of risk. In this paper, we propose a stochastic SEIHR (sSEIHR) model to describe such random fluctuations and provide sufficient conditions for stochastic stability of the disease-free equilibrium, based on the basic reproduction number that we estimated. Our extensive numerical results demonstrate strong threshold behavior near the estimated basic reproduction number, suggesting that the necessary conditions for stochastic stability are close to the sufficient conditions derived. Furthermore, we found that increasing the noise level slightly reduces the final proportion of infected individuals. In addition, we analyze COVID-19 data from various regions worldwide and demonstrate that by changing only a few parameter values, our sSEIHR model can accurately describe both the general trend and the random fluctuations in the number of daily new cases in each region, allowing governments and hospitals to make more accurate caseload predictions using fewer compartments and parameters than other comparable stochastic compartmental models.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nonlinear Dyn Año: 2021 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nonlinear Dyn Año: 2021 Tipo del documento: Article Pais de publicación: Países Bajos