A Mathematical Model to Estimate the Dynamics of the Covid-19 Pandemic Using the Ongoing Public Domain Data

Abstract

At the end of December 2019, it was found that a new coronavirus (SARS-CoV-2) was causing pneumonia-like illness in the city of Wuhan, China. This virus started to spread very rapidly causing a global large-scale infection. The Covid-19 pandemic has produced and it is still generating a brutal impact on society, forcing the lockdown of many countries as well as the collapse of their healthcare system, leading to a considerable growth in the number of deaths. During the outbreak, most of the information and dynamics of the virus was unknown and unpredictable. Therefore, the proposed study aims to create a stochastic mathematical model based on probabilities to estimate the dynamics of the outbreak of the Covid-19 pandemic using the available public domain data. By estimating the probabilities of getting the infection and subsequently recovering or dying from it, the epidemic curves of the cumulative sum of detected infected cases, recoveries and deaths were simulated for Germany, Italy and South Korea from 22nd January to 30th June 2020. Furthermore, using the outputs provided by the proposed model, a more accurate case fatality ratio was calculated and different lockdown scenarios such as its anticipation or delay were discussed. Results have been analyzed with respect to the political and healthcare strategies that each country has followed during the pandemic.