An Improved Agent-based model with Scenario Modeling Functional and Digital Twin Properties for Forecasting Socio-epidemiological-economic processes in the Regions of Russia

Abstract

The article continues the series of works by the authors devoted to the analysis of the socio-epidemiological and economic consequences resulting from the spread of the coronavirus infection COVID-19. In previous studies, a model architecture and three fully functional blocks were developed - demographic, epidemiological and economic. This stage of work allowed us to modernize the agent-based model and make significant changes to the demographic and economic blocks. The modifications added critical details to the experimental design to reproduce a more accurate prediction. The demographic cycles of natural reproduction of the population were specified, the logic of economic processes in the context of the working population of the regions was improved, the basic characteristics of agents underwent changes, and an individual “passport” was created for each human agent. Thus, fully functional blocks have become more adaptive, and the interaction of agents with each other has become more complex and realistic. Changing the presets of the starting state and upgrading the blocks served as support for conducting scenario experiments for three forecast options. There was also an assessment of four demonstration indices that reflect the negative effect of the spread of coronavirus infection in the territories of the studied regions of Russia. The virulence factors of a respiratory disease are analyzed, depending on the scenario being played out in terms of changes in the demographic and economic environments of the regions. The model is endowed with the properties of a digital twin, which has the potential to expand functionality and run it on a supercomputer in order to conduct experiments on a 1:1 scale.