The 2018–2020 Ebola outbreak in the Democratic Republic of the Congo is the first to occur in an armed conflict zone. The resulting impact on population movement, treatment centres and surveillance has created an unprecedented challenge for real-time epidemic forecasting. Most standard mathematical models cannot capture the observed incidence trajectory when it deviates from a traditional epidemic logistic curve. We fit seven dynamic models of increasing complexity to the incidence data published in the World Health Organization Situation Reports, after adjusting for reporting delays. These models include a simple logistic model, a Richards model, an endemic Richards model, a double logistic growth model, a multi-model approach and two sub-epidemic models. We analyse model fit to the data and compare real-time forecasts throughout the ongoing epidemic across 29 weeks from 11 March to 23 September 2019. We observe that the modest extensions presented allow for capturing a wide range of epidemic behaviour. The multi-model approach yields the most reliable forecasts on average for this application, and the presented extensions improve model flexibility and forecasting accuracy, even in the context of limited epidemiological data.

中文翻译：

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2019 年 3 月至 10 月刚果民主共和国埃博拉疫情的多模型预测

2018-2020 年在刚果民主共和国爆发的埃博拉疫情是第一次发生在武装冲突地区。由此产生的对人口流动、治疗中心和监测的影响给实时流行病预测带来了前所未有的挑战。大多数标准数学模型在偏离传统流行病逻辑曲线时无法捕捉到观察到的发病轨迹。在对报告延迟进行调整后，我们将七个日益复杂的动态模型拟合到世界卫生组织情况报告中发布的发病率数据中。这些模型包括简单逻辑模型、理查兹模型、地方病理查兹模型、双逻辑增长模型、多模型方法和两个亚流行模型。我们分析了数据的模型拟合，并比较了 2019 年 3 月 11 日至 9 月 23 日 29 周内持续流行的实时预测。我们观察到，所呈现的适度扩展允许捕获广泛的流行行为。多模型方法为此应用程序平均产生了最可靠的预测，并且所提供的扩展提高了模型的灵活性和预测的准确性，即使在有限的流行病学数据的情况下也是如此。