Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.
Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
Sci Rep. 2021 Jan 13;11(1):923. doi: 10.1038/s41598-020-79438-0.
Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems.
全球每年有超过 3.9 亿人感染登革热。由于缺乏广泛使用的有效疫苗，国家控制项目必须依靠医院的准备情况和有针对性的病媒控制来为疫情做准备，因此准确的预测仍然是一个重要目标。许多登革热预测方法都使用与蚊子生态学相关的环境数据来预测疫情何时发生，但结果喜忧参半。相反，作为感染空间传播的重要驱动因素，人类流动性往往被忽视。在这里，我们比较了泰国登革热的时间序列预测，将流行病学数据与从移动电话数据生成的移动模型进行了整合。我们发现，通过人类旅行紧密相连的地理位置遥远的省份，其登革热发病率比距离相同但联系较弱的省份更为相关，并且包含移动性数据可以改进传统的时间序列预测方法。值得注意的是，没有一种单一的模型或模型类别始终优于其他模型。我们为早期预警系统提出了一种自适应、镶嵌式的预测方法。
