Numerical simulations on scale-free and random networks for the spread of COVID-19 in Pakistan

Epidemiology is the study of how and why an infectious disease occurs in a group of peo-ple. Several epidemiological models have been developed to get information on the spread of a dis-ease in society. That information is used to plan strategies to prevent illness and manage patients. But, most of these models consider only random diffusion of the disease and hence ignore the num-ber of interactions among people. To take into account the interactions among individuals, the net-work approach is becoming increasingly popular. It is novel to consider the dynamics of infectious disease using various networks rather than classical differential equation models. In this paper, we numerically simulate the Susceptible-Infected-Recoverd (SIR) model on Barabasi-Albert network and Erd delta s-Re acute accent nyi network to analyze the spread of COVID-19 in Pakistan so that we know the severity of the disease. We also show how a situation becomes alarming if hubs in a network get infected.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).