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Measuring the unknown: an estimator and simulation study for assessing case reporting during epidemics

View ORCID ProfileChristopher I Jarvis, Amy Gimma, View ORCID ProfileFlavio Finger, View ORCID ProfileTim P Morris, Jennifer A Thompson, Olivier le Polain de Waroux, W John Edmunds, Sebastian Funk, View ORCID ProfileThibaut Jombart
doi: https://doi.org/10.1101/2021.02.17.431606
Christopher I Jarvis
1Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
2Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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  • For correspondence: christopher.jarvis@lshtm.ac.uk thibautjombart@gmail.com
Amy Gimma
1Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
2Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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Flavio Finger
1Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
2Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
3Epicentre, Paris, France
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Tim P Morris
4MRC Clinical Trials Unit at UCL, London, United Kingdom
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Jennifer A Thompson
1Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
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Olivier le Polain de Waroux
2Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
5Public Health England, London, United Kingdom
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W John Edmunds
1Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
2Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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Sebastian Funk
1Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
2Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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Thibaut Jombart
1Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
2Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
6MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
7UK Public Health Rapid Support Team, London, United Kingdom
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  • For correspondence: christopher.jarvis@lshtm.ac.uk thibautjombart@gmail.com
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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.

Author summary When responding to epidemics of infectious diseases, it is essential to estimate how many cases are not being reported. Unfortunately reporting, the proportion of cases actually observed, is difficult to estimate during an outbreak, as it typically requires large surveys to be conducted on the affected populations. Here, we introduce a method for estimating reporting from case investigation data, using the proportion of cases with a known, reported infector. We used simulations to test the performance of our approach by mimicking features of a recent Ebola epidemic in the Democratic Republic of the Congo. We found that despite some uncertainty in smaller outbreaks, our approach can be used to obtain informative ballpark estimates of reporting under most settings. This method is simple and computationally inexpensive, and can be used to inform the response to any epidemic in which transmission events can be uncovered by case investigation.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 17, 2021.
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Measuring the unknown: an estimator and simulation study for assessing case reporting during epidemics
Christopher I Jarvis, Amy Gimma, Flavio Finger, Tim P Morris, Jennifer A Thompson, Olivier le Polain de Waroux, W John Edmunds, Sebastian Funk, Thibaut Jombart
bioRxiv 2021.02.17.431606; doi: https://doi.org/10.1101/2021.02.17.431606
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Measuring the unknown: an estimator and simulation study for assessing case reporting during epidemics
Christopher I Jarvis, Amy Gimma, Flavio Finger, Tim P Morris, Jennifer A Thompson, Olivier le Polain de Waroux, W John Edmunds, Sebastian Funk, Thibaut Jombart
bioRxiv 2021.02.17.431606; doi: https://doi.org/10.1101/2021.02.17.431606

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