Bridging the COVID-19 data and the epidemiological model using time varying parameter SIRD model

Arbeitspapier

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 generalized autoregressive score 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 as well. Results suggest that these cases' effects on the parameter estimates diminish with the increasing number of testing. Full sample results show that the flexible framework captures the successive waves of the pandemic accurately. 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 confirmed cases.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2013

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Construction and Estimation ; Health: Other
• Subject: Covid-19 ; SIRD ; Observation driven models ; Score models ; Count data ; Time-varying parameters

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Çakmaklı, Cem ; Şimşek, Yasin
• Event: Veröffentlichung
• (who): Koç University-TÜSIAD Economic Research Forum (ERF)
• (where): Istanbul
• (when): 2021

Further information

• Handle: http://hdl.handle.net/10419/242997
• Last update: 10.03.2025, 11:44 AM CET