Vol 20, No 5 (2021)
Mathematical modeling and applied mathematics
Forecasting Development of COVID-19 Epidemic in European Union Using Entropy-Randomized Approach
Abstract
The paper is devoted to the forecasting of the COVID-19 epidemic by the novel method of randomized machine learning. This method is based on the idea of estimation of probability distributions of model parameters and noises on real data. Entropy-optimal distributions correspond to the state of maximum uncertainty which allows the resulting forecasts to be used as forecasts of the most "negative" scenario of the process under study. The resulting estimates of parameters and noises, which are probability distributions, must be generated, thus obtaining an ensemble of trajectories that considered to be analyzed by statistical methods. In this work, for the purposes of such an analysis, the mean and median trajectories over the ensemble are calculated, as well as the trajectory corresponding to the mean over distribution values of the model parameters. The proposed approach is used to predict the total number of infected people using a three-parameter logistic growth model. The conducted experiment is based on real COVID-19 epidemic data in several countries of the European Union. The main goal of the experiment is to demonstrate an entropy-randomized approach for predicting the epidemic process based on real data near the peak. The significant uncertainty contained in the available real data is modeled by an additive noise within 30%, which is used both at the training and predicting stages. To tune the hyperparameters of the model, the scheme is used to configure them according to a testing dataset with subsequent retraining of the model. It is shown that with the same datasets, the proposed approach makes it possible to predict the development of the epidemic more efficiently in comparison with the standard approach based on the least-squares method.
Informatics and Automation. 2021;20(5):1010-1033
1010-1033
Balance Model of COVID-19 Epidemic Based on Percentage Growth Rate
Abstract
The paper examines the possibility of using an alternative approach to predicting statistical indicators of a new COVID-19 virus type epidemic. A systematic review of models for predicting epidemics of new infections in foreign and Russian literature is presented. The accuracy of the SIR model for the spring 2020 wave of COVID-19 epidemic forecast in Russia is analyzed. As an alternative to modeling the epidemic spread using the SIR model, a new CIR discrete stochastic model is proposed based on the balance of the epidemic indicators at the current and past time points. The new model describes the dynamics of the total number of cases (C), the total number of recoveries and deaths (R), and the number of active cases (I). The system parameters are the percentage increase in the C(t) value and the characteristic of the dynamic balance of the epidemiological process, first introduced in this paper. The principle of the dynamic balance of epidemiological process assumes that any process has the property of similarity between the value of the total number of cases in the past and the value of the total number of recoveries and deaths at present. To calculate the values of the dynamic balance characteristic, an integer linear programming problem is used. In general, the dynamic characteristic of the epidemiological process is not constant. An epidemiological process the dynamic characteristic of which is not constant is called non-stationary. To construct mid-term forecasts of indicators of the epidemiological process at intervals of stationarity of the epidemiological process, a special algorithm has been developed. The question of using this algorithm on the intervals of stationarity and non-stationarity is being examined. Examples of the CIR model application for making forecasts of the considered indicators for the epidemic in Russia in May-June 2020 are given.
Informatics and Automation. 2021;20(5):1034-1065
1034-1065
Approach for the COVID-19 Epidemic Source Localization in Russia Based on Mathematical Modeling
Abstract
The paper presents the results of statistical data from open sources on the development of the COVID-19 epidemic processing and a study сarried out to determine the place and time of its beginning in Russia. An overview of the existing models of the processes of the epidemic development and methods for solving direct and inverse problems of its analysis is given. A model for the development of the COVID-19 epidemic via a transport network of nine Russian cities is proposed: Moscow, St. Petersburg, Nizhny Novgorod, Rostov-on-Don, Krasnodar, Yekaterinburg, Novosibirsk, Khabarovsk and Vladivostok. The cities are selected both by geographic location and by the number of population. The model consists of twenty seven differential equations. An algorithm for reverse analysis of the epidemic model has been developed. The initial data for solving the problem were the data on the population, the intensity of process transitions from one state to another, as well as data on the infection rate of the population at given time moments. The paper also provides the results of a detailed analysis of the solution approaches to modeling the development of epidemics by type of model (basic SEIR model, SIRD model, adaptive behavioral model, modified SEIR models), and by country (in Poland, France, Spain, Greece and others) and an overview of the applications that can be solved using epidemic spread modeling. Additional environmental parameters that affect the modeling of the spread of epidemics and can be taken into account to improve the accuracy of the results are considered. Based on the results of the modeling, the most likely source cities of the epidemic beginning in Russia, as well as the moment of its beginning, have been identified. The reliability of the estimates obtained is largely determined by the reliability of the statistics used on the development of COVID-19 and the available data on transportation network, which are in the public domain.
Informatics and Automation. 2021;20(5):1066-1090
1066-1090
Artificial intelligence, knowledge and data engineering
Use of Fuzzy Coalition Games in Socially Oriented Decision Making During Hospitalization in Pandemic
Abstract
The problems of organizing medical care in the context of the COVID-19 pandemic, associated with the uncertainty and limitedness of various resources, led to the need to improve decision-making systems for hospitalization of patients. Situational management can improve the decision-making process to fit the current situation better. At the same time, it becomes important to take into account the influence of psychological factors on decisions made during hospitalization. The paper proposes the use of coalition games for situational management during hospitalization of patients. The players and members of the coalition are hospitals, ambulance teams, patients and computed tomography centers. The goal of the game is to form a coalition of participants that provides the maximum benefit in terms of time and cost of hospitalization at the time of decision making. The general scheme of hospitalization, the main sources of information about the situation, the formulation and formalization of the problem are considered. An experiment was carried out in which the formation of a coalition during hospitalization was tested based on data obtained from analyzing the dynamics of the COVID-19 pandemic. Due to the small amount of data and the lack of approved models of the situation development, when carrying out the calculation, some of the parameters were estimated using heuristic models of the development of the situation, based on the analysis of information from open sources of information. The experiment result contains a set of coalitions that provide the maximum benefit under the specified constraints. At the same time, the calculation time of the coalition game allows using the proposed model of decision-making support during hospitalization in the dispatch service of ambulance stations.
Informatics and Automation. 2021;20(5):1091-1116
1091-1116
Analytical Review of Audiovisual Systems for Determining Personal Protective Equipment on a Person's Face
Abstract
Since 2019 all countries of the world have faced the rapid spread of the pandemic caused by the COVID-19 coronavirus infection, the fight against which continues to the present day by the world community. Despite the obvious effectiveness of personal respiratory protection equipment against coronavirus infection, many people neglect the use of protective face masks in public places. Therefore, to control and timely identify violators of public health regulations, it is necessary to apply modern information technologies that will detect protective masks on people's faces using video and audio information. The article presents an analytical review of existing and developing intelligent information technologies for bimodal analysis of the voice and facial characteristics of a masked person. There are many studies on the topic of detecting masks from video images, and a significant number of cases containing images of faces both in and without masks obtained by various methods can also be found in the public access. Research and development aimed at detecting personal respiratory protection equipment by the acoustic characteristics of human speech is still quite small, since this direction began to develop only during the pandemic caused by the COVID-19 coronavirus infection. Existing systems allow to prevent the spread of coronavirus infection by recognizing the presence/absence of masks on the face, and these systems also help in remote diagnosis of COVID-19 by detecting the first symptoms of a viral infection by acoustic characteristics. However, to date, there is a number of unresolved problems in the field of automatic diagnosis of COVID-19 and the presence/absence of masks on people's faces. First of all, this is the low accuracy of detecting masks and coronavirus infection, which does not allow for performing automatic diagnosis without the presence of experts (medical personnel). Many systems are not able to operate in real time, which makes it impossible to control and monitor the wearing of protective masks in public places. Also, most of the existing systems cannot be built into a smartphone, so that users be able to diagnose the presence of coronavirus infection anywhere. Another major problem is the collection of data from patients infected with COVID-19, as many people do not agree to distribute confidential information.
Informatics and Automation. 2021;20(5):1117-1154
1117-1154
Information Technologies of Digital Adaptive Medicine
Abstract
The article provides a comprehensive description of information technologies of digital adaptive medicine. The emphasis is on the applicability to the development of specialized automated complexes, software models and systems for studying the adaptive capabilities of a person to environmental conditions. Requirements for information technologies to enhance these capabilities are formulated. The features of information technologies are reflected in relation to the implementation of applied systemic studies of life support, preservation of professional health and prolongation of human longevity.
Six basic concepts of adaptive medicine with an emphasis on the features of the mathematical support for information processing are characterized, priorities for improving information technologies used in these concepts are determined.
The information technologies used in the tasks of ensuring the professional performance of a person with an emphasis on the need to use adequate methods for diagnosing the state of a person at all stages of professional activity and the need to develop technologies for digital twins that adequately simulate the adaptation processes and reactions of the body in real conditions are considered.
The characteristics of information technologies for personalized monitoring of health risks are given, which make it possible to objectify the effects of physical factors of the conditions of activity and to implement individual and collective informing of personnel about environmental hazards.
The urgent need to standardize information processing methods in the development of information technologies for digital adaptive medicine in the interests of ensuring physiological adequacy and mathematical correctness of approaches to obtaining and processing information about a person's state is shown.
It is concluded that the priorities for improving information technologies of digital adaptive medicine are associated with the implementation of the achievements of the fourth industrial revolution, including the concept of sociocyberphysical systems.
Informatics and Automation. 2021;20(5):1155-1182
1155-1182