Estimating the undetected infections in the Covid-19 outbreak by harnessing capture-recapture methods
Estimating the undetected infections in the Covid-19 outbreak by harnessing capture-recapture methods
Objectives: A major open question, affecting the decisions of policy makers, is the estimation of the true number of Covid-19 infections. Most of them are undetected, because of a large number of asymptomatic cases. We provide an efficient, easy to compute and robust lower bound estimator for the number of undetected cases. Methods: A modified version of the Chao estimator is proposed, based on the cumulative time-series distributions of cases and deaths. Heterogeneity has been addressed by assuming a geometrical distribution underlying the data generation process. An (approximated) analytical variance of the estimator has been derived to compute reliable confidence intervals at 95% level. Results: A motivating application to the Austrian situation is provided and compared with an independent and representative study on prevalence of Covid-19 infection. Our estimates match well with the results from the independent prevalence study, but the capture–recapture estimate has less uncertainty involved as it is based on a larger sample size. Results from other European countries are mentioned in the discussion. The estimated ratio of the total estimated cases to the observed cases is around the value of 2.3 for all the analyzed countries. Conclusions: The proposed method answers to a fundamental open question: “How many undetected cases are going around?”. CR methods provide a straightforward solution to shed light on undetected cases, incorporating heterogeneity that may arise in the probability of being detected.
Chao's lower bound, Covid-19, Population heterogeneity, Undetected cases
197-201
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Maruotti, Antonello
53159118-f31e-4f3e-b812-dff432d74229
Rocchetti, Irene
860f3ca0-8363-4fb4-b306-9a70e74bb663
Holling, Heinz
4f0c7a5e-ae1d-4ecf-b341-250998033221
August 2020
Bohning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Maruotti, Antonello
53159118-f31e-4f3e-b812-dff432d74229
Rocchetti, Irene
860f3ca0-8363-4fb4-b306-9a70e74bb663
Holling, Heinz
4f0c7a5e-ae1d-4ecf-b341-250998033221
Bohning, Dankmar, Maruotti, Antonello, Rocchetti, Irene and Holling, Heinz
(2020)
Estimating the undetected infections in the Covid-19 outbreak by harnessing capture-recapture methods.
International Journal of Infectious Diseases, 97, .
(doi:10.1016/j.ijid.2020.06.009).
Abstract
Objectives: A major open question, affecting the decisions of policy makers, is the estimation of the true number of Covid-19 infections. Most of them are undetected, because of a large number of asymptomatic cases. We provide an efficient, easy to compute and robust lower bound estimator for the number of undetected cases. Methods: A modified version of the Chao estimator is proposed, based on the cumulative time-series distributions of cases and deaths. Heterogeneity has been addressed by assuming a geometrical distribution underlying the data generation process. An (approximated) analytical variance of the estimator has been derived to compute reliable confidence intervals at 95% level. Results: A motivating application to the Austrian situation is provided and compared with an independent and representative study on prevalence of Covid-19 infection. Our estimates match well with the results from the independent prevalence study, but the capture–recapture estimate has less uncertainty involved as it is based on a larger sample size. Results from other European countries are mentioned in the discussion. The estimated ratio of the total estimated cases to the observed cases is around the value of 2.3 for all the analyzed countries. Conclusions: The proposed method answers to a fundamental open question: “How many undetected cases are going around?”. CR methods provide a straightforward solution to shed light on undetected cases, incorporating heterogeneity that may arise in the probability of being detected.
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More information
Accepted/In Press date: 4 June 2020
e-pub ahead of print date: 11 June 2020
Published date: August 2020
Additional Information:
Funding Information:
We thank Professor Herwig Friedl for a critical reading of the paper as well as pointing out some valuable improvements. We also express deep thanks to an anonymous referee for his/her very valuable comments.
Publisher Copyright:
© 2020 The Authors
Keywords:
Chao's lower bound, Covid-19, Population heterogeneity, Undetected cases
Identifiers
Local EPrints ID: 441245
URI: http://eprints.soton.ac.uk/id/eprint/441245
ISSN: 1201-9712
PURE UUID: c430775a-7b2e-49a5-b0db-5638e595a559
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Date deposited: 08 Jun 2020 16:30
Last modified: 17 Mar 2024 05:38
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Contributors
Author:
Antonello Maruotti
Author:
Irene Rocchetti
Author:
Heinz Holling
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