Modeling COVID‐19: Forecasting and analyzing the dynamics of the outbreaks in Hubei and Turkey

Abstract

As the pandemic of Coronavirus Disease 2019 (COVID‐19) rages worldwide, accurate modeling of the dynamics thereof is essential. However, since the availability and quality of data vary dramatically from region to region, accurate modeling directly from a global perspective is difficult. Nevertheless, via local data collected by certain regions, it is possible to develop accurate local prediction tools, which may be coupled to develop global models. In this study, we analyze the dynamics of local outbreaks of COVID‐19 via a system of ordinary differential equations (ODEs). Utilizing a large amount of data available from the ebbing outbreak in Hubei, China, as a testbed, we predict the trajectory of daily cases, daily deaths, and other features of the Hubei outbreak. Through numerical experiments, we observe the effects of social distancing on the dynamics of the outbreak. Using knowledge gleaned from the Hubei outbreak, we apply our model to analyze the dynamics of the outbreak in Turkey. We provide forecasts for the peak of the outbreak and the daily number of cases and deaths in Turkey, by varying levels of social distancing and the transition rate which is from infected class to confirmed infected class.