Uncertain SEIAR model for COVID-19 cases in China

The Susceptible-Exposed-Infectious-Asymptomatic-Removed (SEIAR) epidemic model is one of most frequently used epidemic models. As an application of uncertain differential equations to epidemiology, an uncertain SEIAR model is derived which considers the human uncertainty factors during the spread of...

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Bibliographic Details
Published inFuzzy optimization and decision making Vol. 20; no. 2; pp. 243 - 259
Main Authors Jia, Lifen, Chen, Wei
Format Journal Article
LanguageEnglish
Published New York Springer US 01.06.2021
Springer Nature B.V
Subjects
Artificial Intelligence
Calculus of Variations and Optimal Control; Optimization
Coronaviruses
COVID-19
Differential equations
Epidemics
Epidemiology
Mathematical Logic and Foundations
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Parameter uncertainty
Probability Theory and Stochastic Processes
China
COVID-19
Parameter estimation
Uncertain SEIAR model
Uncertain differential equation
Uncertainty theory
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ISSN1568-4539
1573-2908
DOI10.1007/s10700-020-09341-w

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Summary:The Susceptible-Exposed-Infectious-Asymptomatic-Removed (SEIAR) epidemic model is one of most frequently used epidemic models. As an application of uncertain differential equations to epidemiology, an uncertain SEIAR model is derived which considers the human uncertainty factors during the spread of an epidemic. The parameters in the uncertain epidemic model are estimated with the numbers of COVID-19 cases in China, and a prediction to the possible numbers of active cases is made based on the estimates.
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ISSN:1568-4539
1573-2908
DOI:10.1007/s10700-020-09341-w