Analysis of COVID-19 prevalence in Belarus, Thailand, and Lithuania utilizing the SIR model during the COVID era

Aim: Since its emergence in 2019, coronavirus disease 2019 (COVID-19) has evolved into a global pandemic, placing extraordinary strain on healthcare systems and societies worldwide. Accurate forecasting of COVID-19 case trends is essential for effective public health planning and intervention. Methods: This study employs the Susceptible-Infectious-Recovered (SIR) model to predict the progression of COVID-19 in three countries: Belarus, Thailand, and Lithuania. Instead of relying on static or globally derived estimates, the model parameters—infection rate (β) and removal rate (γ)—were dynamically calculated for distinct time periods in each country, using country-specific data extracted from Worldometer. This segmented approach accounts for temporal changes in transmission dynamics and public health responses. Results: The country-specific, phase-based parameter estimation improved the model’s alignment with real-world COVID-19 trends observed in Belarus, Thailand, and Lithuania. The refined forecasts closely matched the actual progression patterns in each country, demonstrating the value of adapting parameter estimates to local epidemiological contexts. Conclusions: The proposed approach enhances the predictive accuracy of the SIR model, providing a practical and adaptable framework for forecasting COVID-19 trends in countries with varying pandemic responses. These findings highlight the importance of dynamic parameter adjustment when applying mathematical models to evolving public health crises, ensuring more reliable projections to guide decision-making.