Quantifying the Unseen: Epidemiological Underestimation Problem for COVID-19
Öz
Reliable epidemiological data is a prerequisite for meaningful economic analysis of pandemic-related policies, as it provides the foundation for evaluating public health measures and their economic impacts. In Türkiye, the government did not disclose the number of all confirmed COVID-19 cases for several months after the relaxation of initial mobility restrictions in June 2020, creating significant challenges for assessing the economic and health tradeoffs of these policies. This paper addresses this issue by developing a system dynamics approach that can identify and quantify epidemiological underestimation under extreme data limitations. Our simulation algorithm builds on a nonlinear dynamical model that explicitly accounts for individuals that are exposed but not yet infectious and requires only a few reliable data points. Results imply large deviations between official and estimated figures, and counterfactual experiments show that social distancing, if practiced well and long enough, would have been highly effective for the containment of COVID-19.
Anahtar Kelimeler
nonlinear systems, SEIRD model, underreporting, social distancing
Kaynakça
- Adıgüzel, F. S., Cansunar, A., & Çörekçioğlu, G. (2020). Truth or dare? detecting systematic manipulation of covid-19 statistics. Journal of Political Institutions and Political Economy, 1(4), 543-557.
- Apple. (2021). Mobility trends reports. https://www.apple.com/covid19/mobility.
- Attar, M. A., & Tekin-Koru, A. (2022). Latent social distancing: Identification, causes and consequences. Economic Systems, 46(1), 100944.
- Avery, C., Bossert, W., Clark, A. T., Ellison, G., & Ellison, S. F. (2020). Policy implications of models of the spread of coronavirus: Perspectives and opportunities for economists. Covid Economics: Vetted and Real-Time Papers, 1(12).
- Balashov, V. S., Yan, Y., & Zhu, X. (2020). Are less developed countries more likely to manipulate data during pandemics? evidence from newcomb-benford law. https://ideas.repec.org/p/arx/ papers/2007.14841.html.
- Benford, F. (1938). The law of anomalous numbers. Proceedings of the American Philosophical Society, 78(4), 551-572.
- Çakmaklı, C., Demiralp, S., Özcan, Ş. K., Yeşiltaş, S., & Yıldırım, M. A. (2023). COVID-19 and emerging markets: A SIR model, demand shocks and capital flows. Journal of International Economics, 145, 103825.
- Çakmaklı, C., & Şimşek, Y. (2021). Bridging the covid-19 data and the epidemiological model using time-varying parameter sird model. Koç University-TÜSİAD ERF Working Paper, https://eaf. ku.edu.tr/wp-content/uploads/2021/02/erfwp 2013.pdf.
- Chudik, A., Pesaran, M. H., & Rebucci, A. (2021). Covid-19 time-varying reproduction numbers worldwide: An empirical analysis of mandatory and voluntary social distancing (Tech. Rep.). National Bureau of Economic Research.
- Degue, K. H., & Le Ny, J. (2018). An interval observer for discrete-time seir epidemic models. In 2018 annual american control conference (acc) (p. 5934-5939).