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Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics
The fraction of cases reported, known as ‘reporting’, is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9166360/ https://www.ncbi.nlm.nih.gov/pubmed/35604952 http://dx.doi.org/10.1371/journal.pcbi.1008800 |
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author | Jarvis, Christopher I. Gimma, Amy Finger, Flavio Morris, Tim P. Thompson, Jennifer A. le Polain de Waroux, Olivier Edmunds, W. John Funk, Sebastian Jombart, Thibaut |
author_facet | Jarvis, Christopher I. Gimma, Amy Finger, Flavio Morris, Tim P. Thompson, Jennifer A. le Polain de Waroux, Olivier Edmunds, W. John Funk, Sebastian Jombart, Thibaut |
author_sort | Jarvis, Christopher I. |
collection | PubMed |
description | The fraction of cases reported, known as ‘reporting’, is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed. We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018–2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5–10%) of the true value. Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities. |
format | Online Article Text |
id | pubmed-9166360 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91663602022-06-05 Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics Jarvis, Christopher I. Gimma, Amy Finger, Flavio Morris, Tim P. Thompson, Jennifer A. le Polain de Waroux, Olivier Edmunds, W. John Funk, Sebastian Jombart, Thibaut PLoS Comput Biol Research Article The fraction of cases reported, known as ‘reporting’, is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed. We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018–2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5–10%) of the true value. Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities. Public Library of Science 2022-05-23 /pmc/articles/PMC9166360/ /pubmed/35604952 http://dx.doi.org/10.1371/journal.pcbi.1008800 Text en © 2022 Jarvis et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Jarvis, Christopher I. Gimma, Amy Finger, Flavio Morris, Tim P. Thompson, Jennifer A. le Polain de Waroux, Olivier Edmunds, W. John Funk, Sebastian Jombart, Thibaut Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics |
title | Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics |
title_full | Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics |
title_fullStr | Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics |
title_full_unstemmed | Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics |
title_short | Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics |
title_sort | measuring the unknown: an estimator and simulation study for assessing case reporting during epidemics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9166360/ https://www.ncbi.nlm.nih.gov/pubmed/35604952 http://dx.doi.org/10.1371/journal.pcbi.1008800 |
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