Computer simulation of the dynamics of a spatial susceptible-infected-recovered epidemic model with time delays in transmission and treatment.
Comput Methods Programs Biomed
; 212: 106469, 2021 Nov.
Article
in English
| MEDLINE | ID: covidwho-1471519
ABSTRACT
BACKGROUND AND OBJECTIVE:
In this work, we analyze the spatial-temporal dynamics of a susceptible-infected-recovered (SIR) epidemic model with time delays. To better describe the dynamical behavior of the model, we take into account the cumulative effects of diffusion in the population dynamics, and the time delays in both the Holling type II treatment and the disease transmission process, respectively.METHODS:
We perform linear stability analyses on the disease-free and endemic equilibria. We provide the expression of the basic reproduction number and set conditions on the backward bifurcation using Castillo's theorem. The values of the critical time transmission, the treatment delays and the relationship between them are established.RESULTS:
We show that the treatment rate decreases the basic reproduction number while the transmission rate significantly affects the bifurcation process in the system. The transmission and treatment time-delays are found to be inversely proportional to the susceptible and infected diffusion rates. The analytical results are numerically tested. The results show that the treatment rate significantly reduces the density of infected population and ensures the transition from the unstable to the stable domain. Moreover, the system is more sensible to the treatment in the stable domain.CONCLUSIONS:
The density of infected population increases with respect to the infected and susceptible diffusion rates. Both effects of treatment and transmission delays significantly affect the behavior of the system. The transmission time-delay at the critical point ensures the transition from the stable (low density) to the unstable (high density) domain.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Epidemics
/
Models, Biological
Language:
English
Journal:
Comput Methods Programs Biomed
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
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