Real-time decision-making during emergency disease outbreaks.
Real-time decision-making during emergency disease outbreaks.
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DOI：
10.1371/journal.pcbi.1006202
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发表时间：
2018-07
影响因子：
4.3
通讯作者：
Tildesley MJ
中科院分区：
文献类型：
--
作者：
Probert WJM;Jewell CP;Werkman M;Fonnesbeck CJ;Goto Y;Runge MC;Sekiguchi S;Shea K;Keeling MJ;Ferrari MJ;Tildesley MJ
如果爆发新的传染病，通常会使用数学和模拟模型来评估哪些控制策略可以最大程度地减少流行病的影响，从而为政策提供依据。在此类疫情爆发的早期阶段，巨大的参数不确定性可能会限制模型提供准确预测的能力，而政策制定者没有机会等待数据来缓解这种不确定性状态。然而，对于政策制定者来说，衡量成功的标准是面对不确定性时选择最优控制干预，而不是模型预测的准确性。我们通过将流行病模型与 2001 年英国和 2010 年日本宫崎两次历史性口蹄疫爆发中每周间隔观察到的、空间明确的感染数据进行拟合来模拟实时决策过程，并比较在相关时间点切换到替代控制干预措施的影响的前向模拟。将这些建议与使用整个疫情爆发的数据事后得出的政策建议进行比较，从而将我们当时可以做的最好的事情与事后我们可以做的最好的事情进行比较。我们的结果表明，尽管流行病规模的预测存在很大差异，但使用所有数据选择的控制政策也可以在疫情爆发的早期阶段仅使用可用数据来确定。至关重要的是，我们发现，是对受感染农场位置的更好了解，而不是对传播参数的估计的改进，推动了对控制干预措施相对绩效的预测的改进。然而，估计未检测到的传染性场所的能力是传输参数不确定性的函数。在这里，我们证明了实时模型拟合和生成预测的必要性，以评估整个疫情爆发期间的替代控制干预措施。我们的结果强调了在疫情爆发时使用模型来为政策提供信息，以及根据整个疫情爆发期间的额外信息进行调整的依赖于国家的干预措施的重要性。通过评估哪些控制策略将最大限度地减少流行病的影响，数学和模拟模型可用于在传染病爆发的早期阶段为政策提供信息。在这些早期阶段，巨大的不确定性可能会限制模型提供准确预测的能力，而政策制定者没有机会等待数据来缓解这种不确定性状态。然而，对于政策制定者来说，最重要的是最优控制干预的选择，而不是模型预测的准确性。我们将流行病模型与 2001 年英国和 2010 年日本宫崎两次口蹄疫历史爆发中每周观察到的、空间明确的感染数据进行拟合，并比较替代控制干预措施影响的前向模拟。这些数据与使用整个疫情爆发的数据事后得出的政策建议进行了比较。我们的结果表明，尽管流行病规模的预测存在很大的不确定性，但从疫情爆发的早期阶段就可以准确地确定最佳控制政策，并且控制策略的相对绩效很大程度上取决于我们对受感染农场位置的了解，而不是改进的传播参数估计。
In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak. Mathematical and simulation models may be used to inform policy in the early stages of an infectious disease outbreak by evaluating which control strategies will minimize the impact of the epidemic. In these early stages, significant uncertainty can limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, what is most important is the selection of the optimal control intervention, rather than accuracy of model predictions. We fit an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of alternative control interventions. These are compared to policy recommendations generated in hindsight using data from the entire outbreak. Our results show that the optimal control policy is identified accurately from an early stage in an outbreak, despite high levels of uncertainty in projections of epidemic size, and that the relative performance of control strategies is strongly mediated by our understanding of the locations of infected farms, rather than improved estimates of transmission parameters.
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