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Data-Driven Susceptible–Infected–Recovered–Dead (SIRD) Modeling Framework for Long-Term Estimation of Critical Epidemiological Parameters of COVID-19 Dynamics in Indonesia
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COVID-19
SIRD model
epidemiological modeling
Indonesia
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Abstract
The COVID-19 pandemic has demonstrated complex nonlinear transmission dynamics that require robust mathematical and data-driven modeling frameworks for long-term epidemiological forecasting. This study proposes a data-driven Susceptible–Infected–Recovered–Dead (SIRD) modeling framework to estimate key epidemiological parameters governing COVID-19 spread in Indonesia. The model integrates classical compartmental epidemiology with data-informed parameter calibration techniques to improve long-term predictive stability under variable intervention scenarios. Building upon established epidemic modeling literature (Annas et al., 2020; Caccavo, 2020), the proposed framework incorporates dynamic transmission rate estimation, mortality-adjusted recovery processes, and time-varying reproduction number analysis. Comparative evaluation with SEIR-based and statistical forecasting approaches demonstrates that the SIRD structure provides a computationally efficient yet sufficiently expressive representation of disease progression trends in resource-constrained environments.
The study further incorporates insights from optimization-based forecasting methods and neural network prediction frameworks to enhance parameter robustness under uncertain reporting conditions (Al-qaness et al., 2020; Nyoni et al., 2020). Validation against Indonesian epidemiological datasets and global benchmarks shows that the proposed framework achieves improved stability in long-term forecasting of infection and mortality trends. The findings highlight the importance of integrating deterministic epidemiological models with adaptive data-driven calibration mechanisms for improved pandemic decision support systems.
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