Strict baselines for Covid-19 forecasting and ML perspective for USA and Russia

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

Mathematical Model of Determining A Risk to the Human Health Along with the Detection of Hazardous States of Urban Atmosphere Pollution Based on Measuring the Current Concentrations of Pollutants

A mathematical model of joint determining the risk to human health and the identification of hazardous states of the polluted urban atmosphere based on the measurement of current concentrations of pollutants was developed. The structure of the model includes two structural units. The input data for structural units are the results of measuring current concentrations of atmospheric pollutants at a checkpoint. The current risk to human health is calculated in the first unit, and recurrent states of atmosphere for early detection of dangerous pollution levels are determined in the second unit. A distinctive feature of the model is the use of only measurements of current concentrations of pollutants in the atmosphere at a control point. Meteorological or other information is not used. That is why the developed model is universal and can be used in any weather conditions and peculiarities of the urban infrastructure. The operation efficiency of the proposed model was tested experimentally using the example of measuring current concentrations of formaldehyde, nitrogen dioxide, and ammonia in the atmosphere of the typical urban infrastructure. It was established that the developed model makes it possible to determine the risk of immediate toxic effects and chronic intoxication for humans, caused by atmospheric pollution. It was proved experimentally that the proposed model makes it possible, together with the identification of relevant risks to human health, to detect hazardous states of the polluted atmosphere, in which pollutants are usually accumulated. It was established that determining the current probability of recurrent conditions of the polluted atmosphere makes it possible with various reliability degrees to detect the possible occurrence of negative effects of atmospheric pollution on human health 6–12 hours beforehan