Essential information: Uncertainty and optimal control of Ebola outbreaks
Essential information: Uncertainty and optimal control of Ebola outbreaks
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DOI：
10.1073/pnas.1617482114
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发表时间：
2017-05-30
影响因子：
11.1
通讯作者：
Shea, Katriona
中科院分区：
文献类型：
--
作者：
Li, Shou-Li;Bjornstad, Ottar N.;Shea, Katriona
及早解决疫情暴发期间的不确定性可导致快速和有效的决策，前提是不确定性影响行动的优先顺序。对2014年埃博拉疫情的预测范围很大，引发了人们对模型实用性的极大担忧和辩论。通过编码和运行37个已发表的埃博拉模型和5个候选干预措施，我们发现，尽管病例数量预测存在很大差异，但管理选项的排名相对一致。减少丧葬传播和减少社区传播通常被列为两个最好的选择。信息价值(VOI)分析表明，通过解决所有特定于模型的不确定性，工作量可以减少11%，其中有关模型结构的信息占减少的82%，而工作量的不确定性仅占12%。我们的研究表明，流行病学上最令人感兴趣的不确定性可能与与管理最相关的不确定性不同。如果目标是改善管理结果，那么研究的重点应该是确定和解决那些最阻碍选择最佳干预措施的不确定性。我们的研究进一步表明，将多个替代模型简化为较少数量的相关组(这里是共享结构)可以简化决策过程，并可能允许更好地整合流行病学建模和政策决策。
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.