PNAS – Proceedings of the National Academy of Sciences of the United States
[Accessed 20 May 2017]
Biological Sciences – Ecology:
Essential information: Uncertainty and optimal control of Ebola outbreaks
Shou-Li Li, Ottar N. Bjørnstad, Matthew J. Ferrari, Riley Mummah, Michael C. Runge, hristopher J. Fonnesbeck, Michael J. Tildesley, illiam J. M. Probert, and Katriona Shea
PNAS 2017 ; published ahead of print May 15, 2017, doi:10.1073/pnas.1617482114
The 2014 Ebola outbreak illustrates the complexities of decision making in the face of explosive epidemics; management interventions must be enacted, despite imperfect or missing information. The wide range in projected caseload generated attention as a source of uncertainty, but debate did not address whether uncertainty affected choice of action. By reevaluating 37 published models, we show that most models concur that reducing funeral transmission and reducing community transmission are robust and effective management actions to minimize projected caseload. Although models disagreed about absolute caseload, this measure has little relevance for evaluating candidate interventions. Our study highlights the importance of projecting the impact of interventions and is applicable to management of other epidemic outbreaks where rapid decision making is critical.
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.