The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling

Abstract

Background: Our study aimed to assess how effective the preventative measures taken by the state authorities during the pandemic were in terms of public health protection and the rational use of material and human resources. Materials and Methods: We utilized a stochastic agent-based model for COVID-19’s spread combined with the WHO-recommended COVID-ESFT version 2.0 tool for material and labor cost estimation. Results: Our long-term forecasts (up to 50 days) showed satisfactory results with a steady trend in the total cases. However, the short-term forecasts (up to 10 days) were more accurate during periods of relative stability interrupted by sudden outbreaks. The simulations indicated that the infection’s spread was highest within families, with most COVID-19 cases occurring in the 26–59 age group. Government interventions resulted in 3.2 times fewer cases in Karaganda than predicted under a “no intervention” scenario, yielding an estimated economic benefit of 40%. Conclusion: The combined tool we propose can accurately forecast the progression of the infection, enabling health organizations to allocate specialists and material resources in a timely manner.