Bridging the Covid-19 data and the epidemiological model using the time-varying parameter SIRD model
将标准SIRD流行病学模型扩展为时变参数形式，利用得分驱动模型处理疫情日度计数数据，实现实时测量和预测，并纳入未报告病例以提升预测准确性。
This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the score-driven modeling structure designed for the typical daily count data related to the pandemic. The resulting specification permits a flexible yet parsimonious model with a low computational cost. The model is extended to allow for unreported cases using a mixed-frequency setting. Results suggest that these cases’ effects on the parameter estimates might be sizeable. Full sample results show that the flexible framework accurately captures the successive waves of the pandemic. A real-time exercise indicates that the proposed structure delivers timely and precise information on the pandemic’s current stance. This superior performance, in turn, transforms into accurate predictions of the death cases and cases treated in Intensive Care Units (ICUs).