Fractional modeling of COVID-19 epidemic model with harmonic mean type incidence rate
Jitsinchayakul Sowwanee,
Zarin Rahat,
Khan Amir,
Yusuf Abdullahi,
Zaman Gul,
Humphries Usa Wannasingha,
Sulaiman Tukur A.
Affiliations
Jitsinchayakul Sowwanee
Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi, 126, Pracha-Uthit Road, Bang Mod, Thung Khru, Bangkok 10140, Thailand
Zarin Rahat
Department of Basic Sciences, University of Engineering and Technology, Peshawar, Pakistan
Khan Amir
Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi, 126, Pracha-Uthit Road, Bang Mod, Thung Khru, Bangkok 10140, Thailand
Yusuf Abdullahi
Department of Computer Engineering, Biruni University, 34010 Istanbul, Turkey
Zaman Gul
Department of Mathematics, University of Malakand, Chakdara Dir, Khyber Pakhtunkhwa, Pakistan
Humphries Usa Wannasingha
Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi, 126, Pracha-Uthit Road, Bang Mod, Thung Khru, Bangkok 10140, Thailand
Sulaiman Tukur A.
Department of Computer Engineering, Biruni University, 34010 Istanbul, Turkey
Coronavirus disease 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). It was declared on March 11, 2020, by the World Health Organization as a pandemic disease. Regrettably, the spread of the virus and mortality due to COVID-19 have continued to increase daily. The study is performed using the Atangana–Baleanu–Caputo operator with a harmonic mean type incidence rate. The existence and uniqueness of the solutions of the fractional COVID-19 epidemic model have been developed using the fixed point theory approach. Along with stability analysis, all the basic properties of the given model are studied. To highlight the most sensitive parameter corresponding to the basic reproductive number, sensitivity analysis is taken into account. Simulations are conducted using the first-order convergent numerical approach to determine how parameter changes influence the system’s dynamic behavior.
