Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, China.
PLoS Negl Trop Dis. 2020 Dec 21;14(12):e0008924. doi: 10.1371/journal.pntd.0008924. eCollection 2020 Dec.
As a mosquito-borne infectious disease, dengue fever (DF) has spread through tropical and subtropical regions worldwide in recent decades. Dengue forecasting is essential for enhancing the effectiveness of preventive measures. Current studies have been primarily conducted at national, sub-national, and city levels, while an intra-urban dengue forecasting at a fine spatial resolution still remains a challenging feat. As viruses spread rapidly because of a highly dynamic population flow, integrating spatial interactions of human movements between regions would be potentially beneficial for intra-urban dengue forecasting.
In this study, a new framework for enhancing intra-urban dengue forecasting was developed by integrating the spatial interactions between urban regions. First, a graph-embedding technique called Node2Vec was employed to learn the embeddings (in the form of an N-dimensional real-valued vector) of the regions from their population flow network. As strongly interacting regions would have more similar embeddings, the embeddings can serve as "interaction features." Then, the interaction features were combined with those commonly used features (e.g., temperature, rainfall, and population) to enhance the supervised learning-based dengue forecasting models at a fine-grained intra-urban scale.
The performance of forecasting models (i.e., SVM, LASSO, and ANN) integrated with and without interaction features was tested and compared on township-level dengue forecasting in Guangzhou, the most threatened sub-tropical city in China. Results showed that models using both common and interaction features can achieve better performance than that using common features alone.
The proposed approach for incorporating spatial interactions of human movements using graph-embedding technique is effective, which can help enhance fine-grained intra-urban dengue forecasting.
登革热是一种蚊媒传染病，近几十年来已在全球热带和亚热带地区传播。登革热预测对于提高预防措施的效果至关重要。目前的研究主要集中在国家、次国家和城市层面，而在精细的空间分辨率上进行城市内登革热预测仍然是一项具有挑战性的任务。由于人口流动的高度动态性，病毒传播迅速，因此整合区域间人类流动的空间相互作用可能有助于城市内登革热预测。
本研究通过整合城市区域之间的空间相互作用，开发了一种增强城市内登革热预测的新框架。首先，使用一种称为 Node2Vec 的图嵌入技术从人口流动网络中学习区域的嵌入（以 N 维实值向量的形式）。由于强相互作用的区域具有更相似的嵌入，因此嵌入可以作为“交互特征”。然后，将交互特征与常用特征（例如温度、降雨量和人口）结合起来，以提高基于监督学习的精细城市内登革热预测模型的性能。
在广州（中国受威胁最大的亚热带城市）的乡镇级登革热预测中，测试并比较了整合和不整合交互特征的预测模型（即 SVM、LASSO 和 ANN）的性能。结果表明，使用通用和交互特征的模型比仅使用通用特征的模型具有更好的性能。
使用图嵌入技术纳入人类流动空间相互作用的方法是有效的，可帮助提高精细的城市内登革热预测。
