ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2017
Key data for outbreak evaluation: building on the Ebola experience.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
51 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review of Integrated Disease Surveillance and Response (IDSR) implementation in the African region.PloS one · 2021Pooled it
- A systematic review and meta-analysis of patient data from the West Africa (2013-16) Ebola virus disease epidemic.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2019Pooled it
- Dynamics of COVID-19 Risk Perception in Indonesia: A Consecutive Cross-sectional Study From 2020 to 2022.Safety and health at work · 2025Article
- Nowcasting epidemic trends using hospital- and community-based virologic test data.Nature communications · 2025Article
- Modelling practices, data provisioning, sharing and dissemination needs for pandemic decision-making: a European survey-based modellers' perspective, 2020 to 2022.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025Article
- Use of the Chainchecker application: Uganda's experience during the 2022 Sudan Virus Disease outbreak.PLOS global public health · 2025Article
- Unified framework for the ingestion of early epidemic data for downstream data analytics.Wellcome open research · 2025Article
- Refining Estimation of the Instantaneous Reproduction Number During Early Pandemic Stages: Addressing Case-Reporting Variability and Serial Interval Uncertainty.American journal of epidemiology · 2024Article
- Bayesian estimation of the time-varying reproduction number for pulmonary tuberculosis in Iran: A registry-based study from 2018 to 2022 using new smear-positive cases.Infectious Disease Modelling · 2024Article
- Using real-time modelling to inform the 2017 Ebola outbreak response in DR Congo.Nature communications · 2024Article
- Using real-time modelling to inform the 2017 Ebola outbreak response in DR Congo.Nature communications · 2024Article
- A COVID-19 Outbreak in a Large Meat-Processing Plant in England: Transmission Risk Factors and Controls.International journal of environmental research and public health · 2023Article
- Lessons from COVID-19 for rescalable data collection.The Lancet. Infectious diseases · 2023Review
- Article
- ModInterv COVID-19: An online platform to monitor the evolution of epidemic curves.Applied soft computing · 2023Article
- Digital public health leadership in the global fight for health security.BMJ global health · 2023Article
- Correcting the reproduction number for time-varying tests: A proposal and an application to COVID-19 in France.PloS one · 2023Article
- Optimal control for a SIR epidemic model with limited quarantine.Scientific reports · 2022Article
- Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics.PLoS computational biology · 2022Article
- An investigation of spatial-temporal patterns and predictions of the coronavirus 2019 pandemic in Colombia, 2020-2021.PLoS neglected tropical diseases · 2022Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'.
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