ABSTRACT

Recently developed methods for the non-parametric estimation of Hawkes point process models facilitate their application for describing and forecasting the spread of epidemic diseases. We use data from the 2014 Ebola outbreak in West Africa to evaluate how well a simple Hawkes point process model can forecast the spread of Ebola virus in Guinea, Sierra Leone, and Liberia. For comparison, SEIR models that fit previously to the same data are evaluated using identical metrics. To test the predictive power of each of the models, we simulate the ability to make near real-time predictions during an actual outbreak by using the first 75% of the data for estimation and the subsequent 25% of the data for evaluation. Forecasts generated from Hawkes models more accurately describe the spread of Ebola in each of the three countries investigated and result in a 38% reduction in RMSE for weekly case estimation across all countries when compared to SEIR models (total RMSE of 59.8 cases/week using SEIR compared to 37.1 for Hawkes). We demonstrate that the improved fit from Hawkes modeling cannot be attributed to overfitting and evaluate the advantages and disadvantages of Hawkes models in general for forecasting the spread of epidemic diseases.

摘要

最近开发的霍克斯点过程模型的非参数估计方法促进了它们在描述和预测流行病传播方面的应用。我们使用 2014 年西非埃博拉疫情的数据来评估简单的霍克斯点过程模型在预测埃博拉病毒在几内亚、塞拉利昂和利比里亚的传播方面的效果。为了进行比较，之前适合相同数据的 SEIR 模型使用相同的指标进行评估。为了测试每个模型的预测能力，我们通过使用前 75% 的数据进行估计和随后的 25% 的数据进行评估来模拟在实际爆发期间进行近乎实时的预测的能力。霍克斯模型生成的预测更准确地描述了埃博拉病毒在所调查的三个国家的传播情况，与 SEIR 模型相比，所有国家每周病例估计的 RMSE 降低了 38%（使用 SEIR 的总 RMSE 为 59.8 例/周相比之下，霍克斯为 37.1）。我们证明了霍克斯模型的改进拟合不能归因于过度拟合，并评估了霍克斯模型在预测流行病传播方面的优缺点。