Data from: Avoidable errors in the modeling of outbreaks of emerging pathogens, with special reference to Ebola

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.

当新发传染病疫情正在暴发时，公共卫生应急响应依赖于关键流行病学指标（epidemiological quantities）的相关信息，例如传播潜能（transmission potential）与传播间隔（serial interval）。当前，越来越多研究将拟合发病数据（incidence data）的传播模型用于估算此类参数，为公共卫生政策制定提供依据。部分被广泛采用的建模实践可能会导致参数估算出现显著误差，进而使得基于模型的预测结果产生偏差。更令人担忧的是，在此类场景下，人们对参数估算与预测结果的置信度往往被过度高估，从而引发潜在的重大误差，且这些误差会被掩盖而难以被发现。所幸，存在若干简单易行且计算成本低廉的替代方案，可规避上述问题。本研究首先通过仿真实验，展示了将确定性模型（deterministic models）拟合至累积发病数据（cumulative incidence data）这一标准建模流程的潜在缺陷；随后，我们基于随机模型（stochastic models），以2014年西非埃博拉病毒病疫情暴发早期的原始发病数据为样本，演示了该替代建模方法。研究结果表明，该方法不仅能够降低估算偏差，还能更精准地量化参数估算与预测结果的不确定性，更为关键的是，模型拟合不佳的问题可被更便捷地诊断出来。最后，本研究总结了若干原则，用于指导未来传染病疫情防控中的建模工作。