Prediction studies of the epidemic peak of coronavirus disease in Japan: From Caputo derivatives to Atangana–Baleanu derivatives
Pushpendra Kumar, Norodin A. Rangaig, Hamadjam Abboubakar, Anoop Kumar, A. Manickam
Abstract
New atypical pneumonia caused by a virus called Coronavirus (COVID-19) appeared in Wuhan, China in December 2019. Unlike previous epidemics due to the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome coronavirus (MERS-CoV), COVID-19 has the particularity that it is more contagious than the other previous ones. In this paper, we try to predict the COVID-19 epidemic peak in Japan with the help of real-time data from January 15 to February 29, 2020 with the uses of fractional derivatives, namely, Caputo derivatives, the Caputo–Fabrizio derivatives, and Atangana–Baleanu derivatives in the Caputo sense. The fixed point theory and Picard–Lindel of approach used in this study provide the proof for the existence and uniqueness analysis of the solutions to the noninteger-order models under the investigations. For each fractional model, we propose a numerical scheme as well as prove its stability. Using parameter values estimated from the Japan COVID-19 epidemic real data, we perform numerical simulations to confirm the effectiveness of used approximation methods by numerical simulations for different values of the fractional-order [Formula: see text], and to give the predictions of COVID-19 epidemic peaks in Japan in a specific range of time intervals.