Neural Networks and Forecasting COVID-19

ISSN 1060-992X, Optical Memory and Neural Networks, 2021, Vol. 30, No. 3, pp. 225–235. © Allerton Press, Inc., 2021. Neural Networks and Forecasting COVID-19 E. Dadyana, * and P. Avetisyanb, ** a Financial University under the Government of the Russian Federation, Moscow, 125167 Russia b Russian-Armenian University, Yerevan, 0051 Republic of Armenia *e-mail: **e-mail: Received May 14, 2021; revised July 10, 2021; accepted July 12, 2021 Abstract—For analysis tasks, time counts are of interest – values recorded at some, usually equidistant, points in time. The calculation can be performed at various intervals: after a minute, an hour, a day, a week, a month, or a year, depending on how much detail the process should be analyzed. In time series analysis problems, we deal with discrete-time, when each observation of a parameter forms a time frame. The same can be said about the behavior of Covid-19 over time. In this paper, we solve the problem of predicting Covid-19 diseases in the world using neural networks. This approach is useful when it is necessary to overcome difficulties related to non-stationarity, incompleteness, unknown distribution of data, or when statistical methods are not completely satisfactory. The problem of forecasting is solved with the help of the analytical platform Deductor Studio, developed by specialists of the company Intersoft Lab of the Russian Federation. When solving this problem, appropriate methods were used to clean the data from noise and anomalies, which ensured the quality of building a predictive model and obtaining forecast values for tens of days ahead. The principle of time series forecasting was also demonstrated: import, seasonal detection, cleaning, smoothing, building a predictive model, and predicting Covid-19 diseases in the world using neural technologies for 30 days ahead. Keywords: time series, forecasting, neural networks, data preprocessing, training and control samples, coronavirus pandemics DOI: 10.3103/S1060992X21030085 1. INTRODUCTION Today, all over the world they are working to create mechanisms for detecting the spread of COVID-19 and its elimination. Predicting the spread of the disease can help solve this serious problem. Observation and analysis of the spread of coronavirus make it possible to assert that humanity is faced with a synchronized process. The data collected and used to develop forecasts is often a time series, that is, it describes the evolution of a process over time. Therefore, to predict the process, it is possible to apply well-known forecasting methods with preliminary analysis and data processing, as well as using neural network technologies. The goal of any forecast is to create a model that allows you to study the future and assess the trends of a factor. The quality of the forecast in this case depends on the presence of the background variable factor, the measurement error of the considered value, and other factors. Formally, the forecasting problem is formulated as follows: find a function f that allows us to estimate the value of the variable x at time (t + d) by its N previous values, so that: x(t + d ) = f ( x(t ), x(t − 1)..., x(t − N + 1)). Usually, d is assumed to be equal to one, i.e., the function f predicts the next value of x. It is already clear that the coronavirus pandemic has affected the economies of all countries of the world. On the one hand, there is a need to solve the problems associated with reducing the consumption of almost all resources that form the basis of the country’s export potential. On the other hand, it is necessary to solve the problem of stimulating the production and consumption of goods and services in the country. In this situation, it is important to obtain predicted values of the COVID-19 coronavirus infection process for specific dates. When analyzing data, forecasting can predict some unknown quantity from a set of related values. Hence, forecasting is performed using data mining tasks such as regression, classification, and clustering. 225 226 DADYAN, AVETISYAN Predicting the spread of coronavirus is essential in developing protective measures and behavioral measures for the population. The problem with modeling such a system is that every day COVID-19 and the number of new potential cases cannot be determined in a simple mathematical equation. There are many reasons for such problems. The spread of human filaments generally depends on various features, depending on both human behavior and the coronavirus’s biological structure. In any case, research needs to be done to biologically describe the coronavirus to develop a medical treatment and model the spread that will help prevent new cases and focus on the places with the greatest potential needs. According to [1], predicting the spread of coronavirus is very important for operational action planning. Unfortunately, coronaviruses are not easy to control, as the speed and reach of their spread depend on many factors, from environmental to social. In [1], the research results on developing a neural network model for predicting the spread of COVID-19 are presented. The prediction process itself is based on the classical approach of training a neural network with a deep architecture using the NAdam training model. For training, the authors of the article used official data from the government and open repositories. In [2], deep learning was used to identify and diagnose patients with COVID-19 using X-ray images of the lungs. The authors presented two algorithms to diagnose the disease: the deep neural network (DNN) on the fractal feature of images and neural network (SNN) methods using lung images directly. The results show that the presented methods allow detecting infected areas of the lungs with high accuracy—83.84%. Several works are devoted to COVID-19 disease detection using neural networks. The authors of [3– 5] propose a method based on a convolutional neural network (CNN) developed using the EfficientNet architecture for automated COVID-19 diagnostics. The architecture of a computerized medical diagnostics system is also proposed to support healthcare professionals in the decision-making process to diagnose diseases. Several important models have been introduced in recent months. In [6], machine learning was applied to evaluate how this stream’s flash will take place. However, predicting the situation in the case of COVID-19 is not easy since many factors determine rapid changes [7]. Therefore, many approaches have been used to help. In [8], the flow prediction was performed using a mathematical model that evaluated undetected Chinese infections. Sometimes even elementary techniques are used. When a solution is needed immediately, we can start predicting based on preprocessing, in which some cases are simply removed for the applied model on the Euclidean network [9]. In Japan, prognostic models also evaluated the first symptoms of the disease [10]. One of Italy’s first models w (...truncated)