Cem Cakmakli, Yasin Simsek
arXiv 3 Jul 2020 · q-bio.PE · publishedJournal of Econometrics (2024) · 10 citations (OpenAlex)
arXiv:2007.02726 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Davis RA, Dunsmuir WTM, Streett SB (2003) Observation-driven models for poisson counts | 0.737 | 3 | 2 | 100% |
| 2 | Manski CF, Molinari F (2020) Estimating the covid-19 infection rate: Anatomy of an inference problem | 0.737 | 3 | 2 | 100% |
| 3 | Allen LJS (2008) An Introduction to Stochastic Epidemic Models | 0.644 | 2 | 2 | 100% |
| 4 | Atkeson A (2020) How deadly is covid-19? understanding the difficulties with estimation of its fatality rate | 0.644 | 2 | 2 | 100% |
| 5 | Grewelle R, De Leo G (2020) Estimating the global infection fatality rate of covid-19 | 0.644 | 2 | 2 | 100% |
| 6 | Korolev I (2020) Identification and estimation of the seird epidemic model for covid-19 | 0.644 | 2 | 2 | 100% |
| 7 | Creal D, Koopman SJ, Lucas A (2013) Generalized autoregressive score models with applications | 0.644 | 2 | 2 | 100% |
| 8 | Fernández-Villaverde J, Jones CI (2020) Estimating and simulating a sird model of covid-19 for many countries, states, and cities | 0.644 | 2 | 2 | 100% |
| 9 | Kermack WO, McKendrick AG (1927) A contribution to the mathematical theory of epidemics | 0.644 | 2 | 2 | 100% |
| 10 | Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, Shaman J (2020) Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (sars-cov-2) | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 38 scored citations.