A fractional-order compartmental model for the spread of the COVID-19 pandemic
| Autor: | Abdul Q. M. Khaliq, T. A. Biala |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Numerical Analysis
Empirical data education.field_of_study Coronavirus disease 2019 (COVID-19) Applied Mathematics Population Parameter estimation and identifiability COVID-19 01 natural sciences SEIR model Time-fractional model 010305 fluids & plasmas Order (exchange) Modeling and Simulation 0103 physical sciences Pandemic Statistics Identifiability 010306 general physics education Sensitivity analysis Basic reproduction number Contact tracing Mathematics Research Paper |
| Zdroj: | Communications in Nonlinear Science & Numerical Simulation |
| ISSN: | 1878-7274 1007-5704 |
| Popis: | We propose a time-fractional compartmental model (SEI A I S HRD) comprising of the susceptible, exposed, infected (asymptomatic and symptomatic), hospitalized, recovered and dead population for the COVID-19 pandemic. We study the properties and dynamics of the proposed model. The conditions under which the disease-free and endemic equilibrium points are asymptotically stable are discussed. Furthermore, we study the sensitivity of the parameters and use the data from Tennessee state (as a case study) to discuss identifiability of the parameters of the model. The non-negative parameters in the model are obtained by solving inverse problems with empirical data from California, Florida, Georgia, Maryland, Tennessee, Texas, Washington and Wisconsin. The basic reproduction number is seen to be slightly above the critical value of one suggesting that stricter measures such as the use of face-masks, social distancing, contact tracing, and even longer stay-at-home orders need to be enforced in order to mitigate the spread of the virus. As stay-at-home orders are rescinded in some of these states, we see that the number of cases began to increase almost immediately and may continue to rise until the end of the year 2020 unless stricter measures are taken. |
| Databáze: | OpenAIRE |
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