Hybrid Method for Simulation of a Fractional COVID-19 Model with Real Case Application

TL;DR

This paper introduces a hybrid fractional-order model for COVID-19 using Atangana--Baleanu--Caputo derivatives, validated with real data from Pakistan, and analyzes parameter sensitivity affecting disease spread.

Contribution

It develops a novel hybrid numerical method combining decomposition and Laplace transform for fractional COVID-19 modeling with real case application.

Findings

Model aligns well with Pakistan's COVID-19 data

Sensitivity analysis identifies key parameters influencing transmission

Numerical results demonstrate the impact of fractional order variations

Abstract

In this research, we provide a mathematical analysis for the novel coronavirus responsible for COVID-19, which continues to be a big source of threat for humanity. Our fractional-order analysis is carried out using a non-singular kernel type operator known as the Atangana--Baleanu--Caputo (ABC) derivative. We parametrize the model adopting available information of the disease from Pakistan in the period 9th April to 2nd June 2020. We obtain the required solution with the help of a hybrid method, which is a combination of the decomposition method and the Laplace transform. Furthermore, a sensitivity analysis is carried out to evaluate the parameters that are more sensitive to the basic reproduction number of the model. Our results are compared with the real data of Pakistan and numerical plots are presented at various fractional orders.