Open AccessDYNAMICAL ANALYSIS OF A NOVEL DISCRETE FRACTIONAL SITRS MODEL FOR COVID-19
Author(s) -
Amr Elsonbaty,
Zulqurnain Sabir,
Rajagopalan Ramaswamy,
Waleed Adel
Publication year - 2021
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x21400351
Subject(s) - covid-19 , stability (learning theory) , pandemic , coronavirus , mathematics , epidemic model , computer science , control theory (sociology) , control (management) , virology , medicine , disease , artificial intelligence , infectious disease (medical specialty) , machine learning , pathology , population , environmental health , outbreak
In this paper, a discrete fractional Susceptible-Infected-Treatment-Recovered-Susceptible (SITRS) model for simulating the coronavirus (COVID-19) pandemic is presented. The model is a modification to a recent continuous-time SITR model by taking into account the possibility that people who have been infected before can lose their temporary immunity and get reinfected. Moreover, a modification is suggested in the present model to correct the improper assumption that the infection rates of both normal susceptible and old aged/seriously diseased people are equal. This modification complies with experimental data. The equilibrium points for the proposed model are found and results of thorough stability analysis are discussed. A full numerical simulation is carried out and gives a better analysis of the disease spread, influences of model’s parameters, and how to control the virus. Comparisons with clinical data are also provided.