The cost of insecurity: from flare-up to control of a major Ebola virus disease hotspot during the outbreak in the Democratic Republic of the Congo, 2019

Jombart, Thibaut; Jarvis, Christopher I; Mesfin, Samuel; Tabal, Nabil; Mossoko, Mathias; Mpia, Luigino Minikulu; Abedi, Aaron Aruna; Chene, Sonia; Forbin, Ekokobe Elias; Belizaire, Marie Roseline Darnycka; Radiguès, Xavier de; Ngombo, Richy; Tutu, Yannick; Finger, Flavio; Crowe, Madeleine; Edmunds, W. John; Nsio, Justus; Yam, Abdoulaye; Diallo, Boubacar; Guèye, Abdou Salam; Ahuka‐Mundeke, Steve; Yao, Michel; Fall, Ibrahima Socé

doi:10.2807/1560-7917.es.2020.25.2.1900735

Cited by 21 publications

(32 citation statements)

References 13 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Amongst these factors, the amount of infections remaining undetected in the affected populations is a crucial indicator for assessing the state of an epidemic, and yet this quantity is often hard to estimate in real time [3–6]. Indeed, estimation of the overall proportion of individuals infected (attack rates) typically requires time-consuming serological surveys [7–9] which may not be achievable in resource-limited, large-scale emergencies such as the 2014-2016 Ebola virus disease (EVD) outbreak in West Africa [10], or the more recent EVD outbreak in Eastern provinces of the Democratic Republic of the Congo (DRC) [11,12].…”

Section: Introductionmentioning

confidence: 99%

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

Jarvis

Gimma

Finger

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Jarvis

Gimma

Finger

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Once past cases are reconstructed, we use a branching process model for forecasting new cases 10,11 . This model combines data on the reproduction number (R) and serial interval distribution to simulate new cases 'y t ' on day 't' from a Poisson distribution:…”

Section: Methodsmentioning

confidence: 99%

“…is the probability mass function of the serial interval distribution. More details on this simulation model can be found in Jombart et al 11 . Optionally, this model can also incorporate heterogeneity in transmissibility using a Negative Binomial distribution instead of Poisson.…”

Section: Methodsmentioning

confidence: 99%

Inferring the number of COVID-19 cases from recently reported deaths

et al. 2020

Self Cite

View full text Add to dashboard Cite

We estimate the number of COVID-19 cases from newly reported deaths in a population without previous reports. Our results suggest that by the time a single death occurs, hundreds to thousands of cases are likely to be present in that population. This suggests containment via contact tracing will be challenging at this point, and other response strategies should be considered. Our approach is implemented in a publicly available, user-friendly, online tool.

show abstract

“…is the probability mass function of the serial interval distribution. More details on this simulation model can be found in Jombart et al (10). Optionally, this model can also incorporate heterogeneity in transmissibility using a Negative Binomial distribution instead of Poisson.…”

Section: Using Deaths To Infer Casesmentioning

confidence: 99%