RESEARCH INTO THE INFLUENCE OF VARIOUS FACTORS ON THE LEVEL OF MORBIDITY DURING A PANDEMIC USING ARTIFICIAL NEURAL NETWORKS AND THE R PROGRAMMING AND DATA ANALYSIS LANGUAGE

Abstract

The problem of analyzing the impact of various factors on the level of morbidity during a pandemic is considered. The task of calculating the effectiveness of anti-epidemic measures and changing the percentage of general patients in general and those who suffered the disease in a severe form is formulated. The input factors of the predictive model are considered to be the “mask regime”, quarantine, distance learning, the possibility of vaccination, the availability of mandatory vaccination, and the percentage of vaccinated people. The output factors are the percentage of total infected and the percentage of those who developed complications after the disease (in the latter case, the percentage of total infected is added to the input factors). The task of calculating the impact of various factors on the level of population morbidity was also formulated using the example of COVID-19 statistics in a number of countries (Brazil, Germany, Japan, Ukraine, and the USA). An analysis of the main input factors was conducted, such as climatic conditions, population density, population age, vaccination level, socio-economic conditions, population size, and measures to counter the pandemic. Data sets based on real indicators were created. The artificial neural network method was used to solve both problems. A script was developed in the R programming and data analysis language. Calculations show that to achieve the best result in predicting the number of total infected, it is necessary to use a perceptron, which has two hidden layers, each of which consists of five neurons. To achieve the best result in predicting the number of seriously ill patients, it is necessary to apply a perceptron, which has three hidden layers, each of which consists of three neurons. In both variants, a sigmoidal activation function is recommended. The first model was used to analyze the level of influence of the listed factors on the level of morbidity. It was found that the minimum impact on determining the change in the number of generally infected people is either the general opportunity to be vaccinated or the combination of a mandatory mask regime with the introduction of distance learning. The minimum weight when calculating the number of seriously ill patients is the combination of the same opportunity to be vaccinated with the introduction of distance learning. In both cases, the maximum impact is the introduction of mandatory vaccination. During the study of the impact of indicators in different countries, it was found that the level of morbidity depends to a large extent on factors such as population density, vaccination level and socio-economic conditions. The results obtained can be used to improve strategies for anti-epidemic measures and improve management decisions in the field of health care.