Essential information: Uncertainty and optimal control of Ebola outbreaks

Abstract

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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