Data analysis and prediction of the COVID-19 outbreak in the first and second waves for top 5 affected countries in the world.

Abstract

In this paper, we introduce a SEIATR compartmental model to analyze and predict the COVID-19 outbreak in the Top 5 affected countries in the world, namely the USA, India, Brazil, France, and Russia. The officially confirmed cases and death due to COVID-19 from the day of the official confirmation to June 30, 2021 are considered for each country. Primarily, we use the data to make a comparison between the cumulative cases and deaths due to COVID-19 among these five different countries. This analysis allows us to infer the key parameters associated with the dynamics of the disease for these five different countries. For example, the analysis reveals that the infection rate is much higher in the USA, Brazil, and France compared to that of India and Russia, while the recovery rate is found almost the same for these countries. Further, the death rate is measured higher in Brazil as opposed to India, where it is found much lower among the remaining countries. We then use the SEIART compartmental model to characterize the first and second waves of these countries, as well as to investigate and identify the influential model parameters and nature of the virus transmissibility in respective countries. Besides estimating the time-dependent reproduction number (Rt) for these countries, we also use the model to predict the peak size and the time occurring peak in respective countries. The analysis demonstrates that COVID-19 was observed to be much more infectious in the second wave than the first wave in all countries except France. The results also demonstrate that the epidemic took off very quickly in the USA, India, and Brazil compared to two other countries considered in this study. Furthermore, the prediction of the epidemic peak size and time produced by our model provides a very good agreement with the officially confirmed cases data for all countries expect Brazil.