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

Cem Çakmaklı and Yasin Simsek

Papers from arXiv.org

Abstract: This paper extends the canonical model of epidemiology, SIRD model, to allow for time varying parameters for real-time measurement of the stance of the COVID-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modelling structure designed for the typically daily count data related to pandemic. The resulting specification permits a flexible yet parsimonious model structure with a very low computational cost. This is especially crucial at the onset of the pandemic when the data is scarce and the uncertainty is abundant. Full sample results show that countries including US, Brazil and Russia are still not able to contain the pandemic with the US having the worst performance. Furthermore, Iran and South Korea are likely to experience the second wave of the pandemic. A real-time exercise show that the proposed structure delivers timely and precise information on the current stance of the pandemic ahead of the competitors that use rolling window. This, in turn, transforms into accurate short-term predictions of the active cases. We further modify the model to allow for unreported cases. Results suggest that the effects of the presence of these cases on the estimation results diminish towards the end of sample with the increasing number of testing.

Date: 2020-07, Revised 2021-02
New Economics Papers: this item is included in nep-cis
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http://arxiv.org/pdf/2007.02726 Latest version (application/pdf)

Related works:
Working Paper: Bridging the COVID-19 Data and the Epidemiological Model using Time Varying Parameter SIRD Model (2021)
Working Paper: Bridging the COVID-19 Data and the Epidemiological Model using Time Varying Parameter SIRD Model (2021)
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