INFORMATION TECHNOLOGY FOR PREDICTING THE LEVEL OF EPIDEMIOLOGICAL DANGER USING NEURONET MODELING - OVCHARUK, OLEKSANDR; MAZURETS, OLEKSANDR; SOBKO, OLENA; MOLCHANOVA, MARYNA; KLIMENKO, VALERIIA (Herald of Khmelnytskyi National University. Technical sciences, 2023)
The problem of epidemiological risk is that infectious diseases can spread rapidly among the population through person-to-person contact, airborne infection, ingestion of food or water, mosquitoes, and other vectors. Increased mobility and globalization are contributing to the rapid spread of infections around the world. The danger of epidemics lies in their impact on public health and socio-economic systems. Large epidemics can have a significant impact on society, causing high mortality, significant economic losses, disruption of production and trade, and social instability. To combat the epidemiological threat, it is necessary to develop strong systems of epidemiological surveillance, rapid response and disease control.The article is devoted to solving the problem of determining the level of epidemiological danger, for which information technology of forecasting the level of epidemiological danger, a method of forecasting parameter values by their time series using a recurrent temporal neural network with a convolutional layer as a component of information technology, and a corresponding software system have been developed. In addition, the article describes the corresponding software system that implements the developed information technology. This system allows users to obtain predictions about the level of epidemiological danger based on entered data on parameters related to epidemiology. It can be useful for organizations involved in the control and forecasting of epidemic diseases, as well as for government structures and medical institutions. The developed information technology and the corresponding software system can contribute to the improvement of forecasting of epidemiological danger, which allows making more informed decisions regarding the prevention and control of epidemic diseases.
Keywords: epidemiological danger recurrent temporal neural network; predicting the level of epidemiological danger; prediction of diseases