Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola.
Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola.
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在新兴病原体爆发的建模中可避免的错误，并特别提及埃博拉病毒。
DOI：
10.1098/rspb.2015.0347
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发表时间：
2015-05-07
期刊：
影响因子：
--
通讯作者：
Rohani P
中科院分区：
文献类型：
--
作者：
King AA;Domenech de Cellès M;Magpantay FM;Rohani P
当突发传染病爆发时，公共卫生应对依赖于关键流行病学量的信息，如传播潜力和连续间隔。越来越多地使用适合发病率数据的传播模型来估计这些参数和指导政策。一些广泛使用的建模做法可能导致参数估计出现很大误差，从而导致基于模型的预测出现误差。更令人担忧的是，在这种情况下，对参数估计和预测的信心本身就可能被高估，导致可能出现掩盖其本身存在的巨大误差。幸运的是，存在简单且计算成本低廉的替代方案，可以避免这些问题。在这里，我们首先使用一个模拟研究，以证明潜在的陷阱的标准做法，拟合确定性模型的累积发病率数据。接下来，我们展示了一种基于随机模型的替代方案，该模型适合2014年西非埃博拉病毒病爆发早期的原始数据。我们不仅表明，偏见，从而减少，但估计和预测的不确定性更好地量化，关键是，缺乏模型拟合更容易诊断。最后，我们总结了一个简短的原则清单，以指导对未来传染病爆发的建模反应。
As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely used modelling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far overestimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola virus disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modelling response to future infectious disease outbreaks.
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影响因子：
15.8
作者：
Wearing HJ;Rohani P;Keeling MJ
通讯作者：
Keeling MJ
DOI：
10.1371/currents.outbreaks.9e4c4294ec8ce1adad283172b16bc908
发表时间：
2014-09-18
期刊：
PLoS currents
影响因子：
--
作者：
Towers, Sherry;Patterson-Lomba, Oscar;Castillo-Chavez, Carlos
通讯作者：
Castillo-Chavez, Carlos
影响因子：
56.9
作者：
Lipsitch, M;Cohen, T;Murray, M
通讯作者：
Murray, M
DOI：
10.1073/pnas.1410597112
发表时间：
2015-01-20
影响因子：
11.1
作者：
Ionides, Edward L.;Dao Nguyen;King, Aaron A.
通讯作者：
King, Aaron A.
影响因子：
2
作者：
Riley, S;Donnelly, CA;Ferguson, NM
通讯作者：
Ferguson, NM