Analysis of Neural Network and Statistical Models Used for Forecasting of a Disease Infection Cases

More than a year has passed since the coronavirus 2 (SARS-CoV2) pandemic began, and no one has been able to forecast the infection cases of the disease with high accuracy. Nowadays, a lot of studies are devoted to finding out the pattern of infection spreading and forecasting cases of infection. But models used in those studies have large errors in forecasts, and this makes it more challenging to discover the pattern of infection spreading. The choice of appropriate models may vary from country to country. The increase of the number of infection cases is one of the global challenges nowadays not only for virusologists and medicians, but also for data analytics. In our paper, we analyze errors in the forecast in the list of the top 10 countries affected by disease on January 1, 2021 using the following the neural network models, linear and non-linear classical statistical models, and also classical epidemiological SIR model. The other part of our computational experiment is devoted to forecasting the dates of the peak values of time series. It is shown on time series for the regions most affected by pandemics that neural network models allow to forecast this date with high accuracy. We also discover the possible field of application of such algorithms in other fields.