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Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation

Faranda, Davide, Castillo, Isaac Pérez, Hulme, Oliver, Jezequel, Aglaé, Lamb, Jeroen S.W., Sato, Yuzuru and Thompson, Erica L. (2020) Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation. Chaos, 30 (5). ISSN 1054-1500

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Identification Number: 10.1063/5.0008834


Despite the importance of having robust estimates of the time-asymptotic total number of infections, early estimates of COVID-19 show enormous fluctuations. Using COVID-19 data from different countries, we show that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data point before the maximum number of daily infections is reached. We propose a physical explanation for this sensitivity, using a susceptible-exposed-infected-recovered model, where the parameters are stochastically perturbed to simulate the difficulty in detecting patients, different confinement measures taken by different countries, as well as changes in the virus characteristics. Our results suggest that there are physical and statistical reasons to assign low confidence to statistical and dynamical fits, despite their apparently good statistical scores. These considerations are general and can be applied to other epidemics.

Item Type: Article
Official URL:
Additional Information: © 2020 AIP Publishing
Divisions: Centre for Analysis of Time Series
Subjects: R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
H Social Sciences > HV Social pathology. Social and public welfare. Criminology
Date Deposited: 29 Oct 2020 13:36
Last Modified: 28 Jun 2024 01:48

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