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

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Title: Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation
Authors: Faranda, D
Castillo, IP
Hulme, O
Jezequel, A
Lamb, JSW
Sato, Y
Thompson, EL
Item Type: Journal Article
Abstract: 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. COVID-19 is currently affecting over 180 countries worldwide and poses serious threats to public health as well as economic and social stability of many countries. Modeling and extrapolating in near real-time the evolution of COVID-19 epidemics is a scientific challenge, which requires a deep understanding of the non-linearities undermining the dynamics of the epidemics. Here, we show that real-time predictions of COVID-19 infections are extremely sensitive to errors in data collection and crucially depend on the last available data point. We test these ideas in both statistical (logistic) and dynamical (susceptible–exposed–infected–recovered) models that are currently used to forecast the evolution of the COVID-19 epidemic. Our goal is to show how uncertainties arising from both poor data quality and inadequate estimations of model parameters (incubation, infection, and recovery rates) propagate to long-term extrapolations of infection counts. We provide guidelines for reporting those uncertainties to the scientific community and the general public.
Issue Date: 19-May-2020
Date of Acceptance: 1-Apr-2020
URI: http://hdl.handle.net/10044/1/79384
DOI: 10.1063/5.0008834
ISSN: 1054-1500
Publisher: AIP Publishing
Start Page: 051107-1
End Page: 051107-10
Journal / Book Title: Chaos: An Interdisciplinary Journal of Nonlinear Science
Volume: 30
Issue: 5
Copyright Statement: © 2020 Author(s). This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article may be found at https://doi.org/10.1063/5.0008834
Sponsor/Funder: The Royal Society
Funder's Grant Number: NAF-R1-180236
Keywords: Fluids & Plasmas
0102 Applied Mathematics
0103 Numerical and Computational Mathematics
0299 Other Physical Sciences
Publication Status: Published
Online Publication Date: 2020-05-19
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Grantham Institute for Climate Change
Imperial College London COVID-19
Faculty of Natural Sciences