Coronavirus Spreading Analysis Using Dynamic Spreading Factor Epidemic Models

Abstract

By the growing number of viruses and also epidemics, predicting and controlling the epidemics have high priority in today's human life. Network theory is a useful instrument for modelling the epidemics. As we can see, some predictions have been proposed for the disease like influenza (N1H1 virus). In this paper we aimed to compare the spreading model of coronavirus with proposed epidemic models. Also, we have shown that informing people using impressive ways such as social networks and also preventing attempts done by the governments affects the transmission rate. So models which are formed based on static transmission rate are not applicable for disease with dynamic transmission rate.