Comparative Data-Driven Study of Deterministic and Stochastic Multigroup Epidemic Models incorporating Diabetes Heterogeneity Using COVID-19 Data
DOI:
https://doi.org/10.26713/jims.v18i3.3777Abstract
In this study, a comparative data-driven analysis of deterministic and stochastic multigroup epidemic models incorporating diabetes heterogeneity is conducted. Both models were calibrated to reported COVID-19 incidence data using nonlinear least-squares estimation and compared using epidemic curve fitting, goodness-of-fit statistics (SSE, RMSE, R2), and model selection criteria (AIC, BIC). The deterministic model achieved better predictive accuracy. The stochastic model captured the inherent randomness and uncertainty of disease transmission by generating multiple epidemic realizations and confidence intervals. Both models indicated that diabetes increases susceptibility to infection and delays recovery. This significantly influences epidemic dynamics. These results offer practical guidance for selecting appropriate epidemic models for forecasting and public health decision-making.
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