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dc.contributor.authorJarvis, CI
dc.contributor.authorGimma, A
dc.contributor.authorFinger, F
dc.contributor.authorMorris, TP
dc.contributor.authorThompson, JA
dc.contributor.authorde Waroux, OlP
dc.contributor.authorEdmunds, WJ
dc.contributor.authorFunk, S
dc.contributor.authorJombart, T
dc.date.accessioned2021-04-11T17:10:50Z
dc.date.available2021-04-11T17:10:50Z
dc.date.issued2021-12-17
dc.date.submitted2021-04-09
dc.identifier.urihttp://hdl.handle.net/10144/619922
dc.description.abstractThe 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.en_US
dc.language.isoenen_US
dc.publisherbioRxiven_US
dc.rightsWith thanks to bioRxiv.en_US
dc.titleMeasuring the unknown: an estimator and simulation study for assessing case reporting during epidemicsen_US
dc.identifier.journalbioRxiven_US
refterms.dateFOA2021-04-11T17:10:51Z


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