Point process models, such as Hawkes and recursive models, have recently been shown to offer improved accuracy over more traditional compartmental models for the purposes of modeling and forecasting the spread of disease epidemics. To explicitly test the performance of these two models in a real-world and ongoing epidemic, we compared the fit of Hawkes and recursive models to outbreak data on Ebola virus disease (EVD) in the Democratic Republic of the Congo in 2018–2020. The models were estimated, and the forecasts were produced, time-stamped, and stored in real time, so that their prospective value can be assessed and to guard against potential overfitting. The fit of the two models was similar, with both models resulting in much smaller errors in the beginning and waning phases of the epidemic and with slightly smaller error sizes on average for the Hawkes model compared with the recursive model. Our results suggest that both Hawkes and recursive point process models can be used in near real time during the course of an epidemic to help predict future cases and inform management and mitigation strategies.

中文翻译：

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刚果民主共和国埃博拉病毒的前瞻性霍克斯模型和递归点过程模型的比较

点过程模型，例如 Hawkes 和递归模型，最近已被证明比更传统的隔室模型提供更高的准确性，用于建模和预测疾病流行的传播。为了在现实世界和持续流行中明确测试这两种模型的性能，我们将 Hawkes 模型和递归模型的拟合与 2018-2020 年刚果民主共和国埃博拉病毒病 (EVD) 的爆发数据进行了比较。对模型进行估计，并实时生成、标记和存储预测，以便评估其预期值并防止潜在的过度拟合。两个模型的拟合相似，与递归模型相比，这两种模型在流行病的开始和减弱阶段产生的误差要小得多，霍克斯模型的平均误差大小也略小。我们的结果表明，霍克斯模型和递归点过程模型都可以在流行病过程中近乎实时地使用，以帮助预测未来的病例并为管理和缓解策略提供信息。