The Research of Mathematical Models for Forecasting Covid-19 Cases

Abotaleb, M. S. A.; Makarovskikh, T.

The world is currently facing a Covid-19 pandemic and that virus is spreading rapidly among people, which leads to an increase in the number of infection cases and also an increase in the number of death cases. This is a huge challenge as this pandemic affected all sectors, and therefore there was important for mathematicians in modelling this epidemic spread in the world to reduce the damage caused by this pandemic and also discovering the pattern of that virus spreading. In our report, time series models are used to obtain estimates of the number of cases of infection and numbers of deaths using ARIMA, Holt’s Linear Trend, BATS, TBATS, and SIR Models. We have developed a new algorithm to use these models and choose the best model for forecasting the number of infections and deaths in terms of the least error of MAPE as standard. We have observed in most of the data that were used in this algorithm that the best models that achieve the least forecast errors are BATS, TBATS, and ARIMA respectively. The experiment was held for the ten countries most affected by the Covid-19, this algorithm was able to detect the data pattern of the virus spreading for every country, besides it is interested in more research and studies on other models. © 2021, Springer Nature Switzerland AG.