Lessons from Ebola: Improving infectious disease surveillance to inform outbreak management

Woolhouse, Mark; Rambaut, Andrew; Kellam, Paul

doi:10.1126/scitranslmed.aab0191

Cited by 111 publications

(73 citation statements)

References 49 publications

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“…It is inevitable that as sequencing costs decrease, accuracy increases, and sequencing instruments become more portable, real-time viral surveillance and molecular epidemiology will be routinely deployed on the front lines of infectious disease outbreaks 10, 12, 14, 34–36 . Although we have shown here that the broad pattern of EBOV spatial movement was discernible from virus genomes derived from samples collected only up until October 2014, there was a notable hiatus in sequencing at this time 35 and the genomes in the present data set from that time were sequenced retrospectively from archived material.…”

Section: Viral Genomics As a Tool For Outbreak Responsementioning

confidence: 99%

Virus genomes reveal factors that spread and sustained the Ebola epidemic

Dudas

Carvalho

Bedford

et al. 2017

Nature

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380

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The 2013–2016 epidemic of Ebola virus disease was of unprecedented magnitude, duration and impact. Analysing 1610 Ebola virus genomes, representing over 5% of known cases, we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic ‘gravity’ model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already set the seeds for an international epidemic, rendering these measures ineffective in curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing they were susceptible to significant outbreaks but at lower risk of introductions. Finally, we reveal this large epidemic to be a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help inform interventions in future epidemics.

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Section: Viral Genomics As a Tool For Outbreak Responsementioning

confidence: 99%

Virus genomes reveal factors that spread and sustained the Ebola epidemic

Dudas

Carvalho

Bedford

et al. 2017

Nature

Self Cite

380

480

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“…We calculated EVPI, which quantifies the maximum achievable improvement in management that could be obtained by resolving uncertainties before the implementation of specific decisions (10,11). It is quantified as EVPI = X n j=1 p j À opt a C a,j Á − opt a X n j=1 p j C a,j , [1] where n is the total number of models, C a,j represents management performance (i.e., caseload in this study) associated with taking intervention a under model j, p j is the weight associated with model j (i.e., the belief that model j is the true model; subject to the constraint that the p j sum to one), and opt a indicates the optimum over all interventions (10,11). In this initial analysis, we weighed the models equally.…”

Section: Methodsmentioning

confidence: 99%

“…value of information | VoI | epidemiological outbreak management | decision making T he devastating 2014 Ebola outbreak in West Africa is the largest ever recorded (1,2). It resulted in 28,646 cases and 11,323 deaths by March 27, 2016 (WHO report; apps.who.int/ebola/ ebola-situation-reports) and engendered an outpouring of concern for those affected.…”

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confidence: 99%

Essential information: Uncertainty and optimal control of Ebola outbreaks

Bjørnstad

Ferrari

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.value of information | VoI | epidemiological outbreak management | decision making T he devastating 2014 Ebola outbreak in West Africa is the largest ever recorded (1, 2). It resulted in 28,646 cases and 11,323 deaths by March 27, 2016 (WHO report; apps.who.int/ebola/ ebola-situation-reports) and engendered an outpouring of concern for those affected. A large number of epidemiological models were developed and published (2-4). To date, we have identified 55 published Ebola models. Most of these models (50 of 55) projected caseloads as the preferred way to predict epidemic trajectory. However, caseload projections varied widely between models, drawing a great deal of attention and causing intense debate (5, 6).Caseload projection is critical for predicting the size of an epidemic and planning management efforts, and it can vary from model to model for several reasons, such as differences in model structure, parameterization, and other assumptions. Despite model-specific variations in caseload projections, a critical question for decision making is whether different models lead to different management recommendations or different rankings of alternative management actions. If all models agree on the optimal management, then differences in projections are not a critical concern for decision making. Otherwise, if models disagree with respect to the ranking of management recommendations, then the optimal intervention is modelspecific, which means that policymakers face the question of which model(s) to rely on to make management decisions;...

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“…Indeed, with the advent of NGS it is now possible to generate pathogen genomic data directly from diagnostic patient samples 2,3,17–27 within days or hours of the sample being taken 25,26 , and in challenging field situations 19,23,25,26 . The resulting large-scale sequence data sets provide new opportunities for the epidemiological investigation of transmission chains and the improvement of outbreak responses 28 . In the case of the 2013–2016 EVD epidemic, the sequence data generated have revealed key aspects of the patterns and processes of EBOV evolution as the epidemic proceeded 2,3,20–22,24–26,29,30 .…”

Section: Introductionmentioning

confidence: 99%

The evolution of Ebola virus: Insights from the 2013–2016 epidemic

Holmes

Dudas

Rambaut

et al. 2016

Nature

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276

236

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Preface The 2013–2016 epidemic of Ebola virus disease in West Africa was of unprecedented magnitude and changed our perspective on this lethal but sporadically emerging virus. This outbreak also marked the beginning of large-scale real-time molecular epidemiology. Herein, we show how evolutionary analyses of Ebola virus genome sequences provided key insights into virus origins, evolution, and spread during the epidemic. We provide basic scientists, epidemiologists, medical practitioners, and other outbreak responders with an enhanced understanding of the utility and limitations of pathogen genomic sequencing. This will be crucially important in our attempts to track and control future infectious disease outbreaks.

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Lessons from Ebola: Improving infectious disease surveillance to inform outbreak management

Cited by 111 publications

References 49 publications

Virus genomes reveal factors that spread and sustained the Ebola epidemic

Virus genomes reveal factors that spread and sustained the Ebola epidemic

Essential information: Uncertainty and optimal control of Ebola outbreaks

The evolution of Ebola virus: Insights from the 2013–2016 epidemic

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