Description project
Development of a method for short-term forecasting of the epidemic situation in organized groups in order to increase the effectiveness of preventive measures and reduce morbidity. The project is being implemented in cooperation with the Federal State Budgetary Institution «Main Center for State Sanitary and Epidemiological Surveillance (Special Purpose)» of the Ministry of Defense of the Russian Federation and the Federal State Budgetary Institution «St. Petersburg Research Institute of Physical Culture».
Publications
Using machine learning methods to forecast infectious diseases
Authors: A.V. Golubkov, A.S. Kucherov, M.P. Gavrilova, I.I. Tokin, V.V. Tsvetkov et al.
Source: Military Medical Journal. 2023;344(9):35-41.
More
The article provides information on the stages of development of artificial intelligence technologies, presents a classification and current aspects of the use of machine learning methods in various areas of professional activity. These methods are widely used in clinical medicine. In medical and preventive care, machine learning methods are used extremely rarely due to the peculiarities of a number of environmental factors, the interaction of macro - and microorganisms, the quality and reliability of a large number of processed data. All this significantly increases the complexity of using these methods in predicting infectious diseases. The article presents preliminary results of our own research devoted to the development of new methods for predicting the level and increase in the incidence of acute respiratory infections in organized groups using one of the machine learning technologies - gradient boosting.
Patents
Daily incidence of acute respiratory infections among students of the Nakhimov Naval School in 2017-2019.
Authors: A.V. Golubkov, M.P. Gavrilova, A.S. Kucherov, I.I. Tokin, V.V. Tsvetkov et al.
Source: Certificate of state registration of the database RU 2024621061. 2024.
More details
The database (DB) contains the results of daily morbidity in study groups and classes with acute respiratory infections of students of the Nakhimov Naval School in the period from January 1, 2017 to December 31, 2019. The DB is intended to assess the intra-annual dynamics and prevalence of morbidity in small organized groups of pre-university educational institutions of the Ministry of Defense of the Russian Federation and can be used by specialists of the centers of state sanitary and epidemiological surveillance of the Ministry of Defense of the Russian Federation, the Ministry of Health, Rospotrebnadzor, healthcare organizers, epidemiologists and pediatricians. The database allows: to determine and quantify the prevalence and incidence rates of acute respiratory infections in groups and classes of students of pre-university educational institutions of the Ministry of Defense of the Russian Federation; to predict the incidence of acute respiratory infections and influenza in groups and classes of students of pre-university educational institutions of the Ministry of Defense of the Russian Federation. Computer type: IBM PC-compatible PC; OS: Windows XP and later versions.