Bridging the Covid-19 Data and the Epidemiological Model using Time-Varying Parameter SIRD Model

TL;DR

This paper introduces a time-varying parameter SIRD model for Covid-19 that captures pandemic dynamics more accurately and efficiently, incorporating unreported cases and providing real-time insights and predictions.

Contribution

It develops a flexible, low-cost, time-varying SIRD model with unreported case extension, improving real-time pandemic tracking and forecasting accuracy.

Findings

Accurately captures multiple waves of Covid-19.

Effectively incorporates unreported cases into the model.

Provides timely and precise pandemic status updates.

Abstract

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 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 confirmed and death cases.