Computer model of differential-symbolic risk assessment of projects to improve the health of the community population

Abstract

The article presents the results of developing a computer model for differential symbolic risk assessment of community health improvement projects. Traditional approaches and methods, such as expert opinions or statistical models, have limitations regarding the accuracy of risk prediction and adaptation to changing conditions of the project environment. The proposed computer model uses a system of differential equations that describe the dynamics of key project indicators, such as public participation in activities, the effectiveness of educational and vaccination measures, budget changes, and their impact on overall risk. This model allows assessing risks taking into account the studied project dynamics, promptly adjusting management decisions, and reducing deviations from the planned indicators. To implement the proposed model, an algorithm has been developed that includes several stages: initialization of variables, construction of a system of differential equations, their numerical solution by the Euler method, risk assessment, and real-time updating of parameters. Based on the developed algorithm, which involves 9 steps, a computer model has been created, which will be further integrated into a decision support system for project managers. The proposed computer model is written in the Python programming language using libraries for solving differential equations, optimization, and visualization of results that implement the proposed mathematical model. This computer model allows project managers to simulate risks, analyze their impact on project performance, and generate recommendations for managing resources and minimizing risks. The developed computer model was tested on the example of real community health improvement projects. For the community vaccination project, the computer model showed a forecasting accuracy of 97.14%, which exceeds the figure for the use of expert estimates (92.86%). In an educational project to promote healthy lifestyles among the community population, the accuracy of the computer model is 90.00% compared to 88.00% when using the method of expert judgment. The risk assessment showed that the use of the differential-symbolic model can reduce the risk level to 2.86% in the community vaccination project and 10.0% in the community health education project. At the same time, traditional methods showed risks of 7.14 % and 12.00 %, respectively. The computer model also proved to be adaptable to the changing project environment, which included an increase in project duration or a decrease in the available budget. The proposed computer model integrates functionality for parameter input, numerical risk calculation, visualization of results, and generation of recommendations. The interface of the computer model is designed in such a way as to provide convenience for project managers, even in conditions of high complexity of input data. The obtained results confirm that the developed computer model for differential symbolic risk assessment of community health improvement projects is an effective tool for project management. The use of the model allows not only to improve the accuracy of risk forecasting but also to ensure efficient resource allocation.