Article
Backcasting COVID-19: A physics-informed estimate for early case incidence
Author/s | Kevrekidis, George A.
Rapti, Zoi Drossinos, Yannis Kevrekidis, Panayotis G. Barmann, Michael A. Chen, Qian-Yong Cuevas-Maraver, Jesús |
Department | Universidad de Sevilla. Departamento de Física Aplicada I |
Publication Date | 2022-12 |
Deposit Date | 2023-01-16 |
Published in |
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Abstract | It is widely accepted that the number of reported cases during the first stages of the COVID-19 pandemic severely underestimates the number of actual cases. We leverage delay embedding theorems of Whitney and Takens and ... It is widely accepted that the number of reported cases during the first stages of the COVID-19 pandemic severely underestimates the number of actual cases. We leverage delay embedding theorems of Whitney and Takens and use Gaussian process regression to estimate the number of cases during the first 2020 wave based on the second wave of the epidemic in several European countries, South Korea and Brazil. We assume that the second wave was more accurately monitored, even though we acknowledge that behavioural changes occurred during the pandemic and region- (or country-) specific monitoring protocols evolved. We then construct a manifold diffeomorphic to that of the implied original dynamical system, using fatalities or hospitalizations only. Finally, we restrict the diffeomorphism to the reported cases coordinate of the dynamical system. Our main finding is that in the European countries studied, the actual cases are under-reported by as much as 50%. On the other hand, in South Korea—which had a proactive mitigation approach—a far smaller discrepancy between the actual and reported cases is predicted, with an approximately 18% predicted underestimation. We believe that our backcasting framework is applicable to other epidemic outbreaks where (due to limited or poor quality data) there is uncertainty around the actual cases. |
Funding agencies | Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía and FEDER Program 2014-2020 P18-RT-3480 Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía and FEDER Program 2014-2020 US-1380977 MCIN/AEI/10.13039/501100011033 PID2019-110430GB-C21 MCIN/AEI/10.13039/501100011033 PID2020-112620GB-I00 |
Project ID. | P18-RT-3480
US-1380977 PID2019-110430GB-C21 PID2020-112620GB-I00 |
Citation | Kevrekidis, G.A., Rapti, Z., Drossinos, Y., Kevrekidis, P.G., Barmann, M.A., Chen, Q. y Cuevas-Maraver, J. (2022). Backcasting COVID-19: A physics-informed estimate for early case incidence. Royal Society Open Science, 9 (12). https://doi.org/10.1098/rsos.220329. |
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