Jarvis, Christopher I;
Gimma, Amy;
Finger, Flavio;
Morris, Tim P;
Thompson, Jennifer A;
Le Polain de Waroux, Olivier;
Edmunds, W John;
... Jombart, Thibaut; + view all
(2022)
Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics.
PLoS Computational Biology
, 18
(5)
, Article e1008800. 10.1371/journal.pcbi.1008800.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pcbi.1008800 |
| Publisher version: | https://doi.org/10.1371/journal.pcbi.1008800 |
| Language: | English |
| Additional information: | Copyright © 2022 Jarvis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| Keywords: | Epidemiological methods and statistics, Epidemiology, Infectious disease epidemiology, Simulation and modeling, Infectious disease surveillance, Probability distribution, Democratic Republic of the Congo, Monte Carlo method |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10149749 |
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