Information technology for forecasting epidemiological threats based on the telegraphic equation

Abstract

Based on the analysis, it is established that traditional approaches, such as the SIR (Susceptible-Infectious-Covered) and SEIR (Susceptible-Exposed-Infectious-Covered) models, do not provide sufficient forecasting accuracy and do not take into account the complex dynamics of the spread of infectious diseases. The need to develop a method that will improve forecasting accuracy and provide support for managerial decision-making to predict the spread of epidemiological threats based on the telegraphic equation is substantiated. The developed system allows for making effective management decisions aimed at reducing the negative impact of the epidemic on the population and medical infrastructure. The use of the telegraphic level allows us to predict the wave spread of infection, spatial and temporal delays, as well as sources of new infections, which ensures accurate forecasting of peak periods, the duration of the epidemic, and the workload of medical facilities. The developed method integrates the classical SIR model with the telegraphic level, which allows the modelling the dynamics of infection spread in a spatio-temporal environment. This method provides forecasting of the spatial and temporal dynamics of infection spread, taking into account wave effects, delays, and the influence of external factors. It provides an opportunity to accurately analyze key epidemic indicators, such as the peak of the disease, its duration, and the distribution of the burden on hospitals. The developed method and mathematical model based on the telegraphic level provided an appropriate level of accuracy in predicting the spatial and temporal dynamics of the spread of epidemiological threats. Testing the model on historical COVID-19 data showed that the average forecast error was 5...10%. This indicates the model's adequacy. In the case of high population mobility, the model accurately described the wave dynamics of the infection. The proposed decision support system includes a user-friendly interface with four tabs for entering model parameters, analyzing results, visualizing them, and generating recommendations. It allows to improve the accuracy of estimating the duration of the epidemic, peak loads, and some resources. The developed system is a tool for managers to support the adoption of governmental decisions aimed at predicting the infection of the population of regions and optimizing the use of medical resources. The results of the study can be used to plan epidemic response measures at the local, regional, and global levels. The proposed system ensures efficiency, flexibility, and accuracy, which are key to managing epidemiological situations in the face of modern challenges.