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 ...
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.
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