King Aaron A, Domenech de Cellès Matthieu, Magpantay Felicia M G, Rohani Pejman
Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.
Proc Biol Sci. 2015 May 7;282(1806):20150347. doi: 10.1098/rspb.2015.0347.
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.
随着新发传染病疫情的发展，公共卫生应对措施依赖于关键流行病学指标的信息，如传播潜力和代间距。越来越多地，拟合发病率数据的传播模型被用于估计这些参数并指导政策制定。一些广泛使用的建模方法会导致参数估计中潜在的巨大误差，进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可能被高估，从而导致可能掩盖其自身存在的巨大误差。幸运的是，存在简单且计算成本低的替代方法可以避免这些问题。在此，我们首先通过模拟研究展示将确定性模型拟合到累积发病率数据的标准做法的潜在缺陷。接下来，我们展示一种基于随机模型的替代方法，该模型拟合了2014年西非埃博拉病毒病疫情早期阶段的原始数据。我们不仅表明由此减少了偏差，而且表明估计和预测中的不确定性得到了更好的量化，并且至关重要的是，更容易诊断出模型拟合不佳的情况。我们最后列出了一系列原则，以指导对未来传染病疫情的建模应对。
