  Currently, the evolution of Covid-19 allows researchers to gather the
datasets accumulated over 2 years and to use them in predictive analysis. In
turn, this makes it possible to assess the efficiency potential of more complex
predictive models, including neural networks with different forecast horizons.
In this paper, we present the results of a consistent comparative study of
different types of methods for predicting the dynamics of the spread of
Covid-19 based on regional data for two countries: the United States and
Russia. We used well-known statistical methods (e.g., Exponential Smoothing), a
"tomorrow-as-today" approach, as well as a set of classic machine learning
models trained on data from individual regions. Along with them, a neural
network model based on Long short-term memory (LSTM) layers was considered, the
training samples of which aggregate data from all regions of two countries: the
United States and Russia. Efficiency evaluation was carried out using
cross-validation according to the MAPE metric. It is shown that for complicated
periods characterized by a large increase in the number of confirmed daily
cases, the best results are shown by the LSTM model trained on all regions of
both countries, showing an average Mean Absolute Percentage Error (MAPE) of
18%, 30%, 37% for Russia and 31%, 41%, 50% for US for predictions at forecast
horizons of 14, 28, and 42 days, respectively.
