Shen Yunpeng, Ren Zhoupeng, Fan Junfu, Xiao Jianpeng, Zhang Yingtao, Liu Xiaobo
School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Insects. 2025 Jun 25;16(7):661. doi: 10.3390/insects16070661.
Generating fine-scale risk maps for mosquito-borne diseases vectors is an essential tool for guiding spatially targeted vector control interventions in urban settings, given the limited public health resources. This study aimed to generate fine-scale risk maps for dengue vectors using routine vector surveillance data collected at the township scale. We integrated monthly township-specific Breteau Index (BI) data from Guangzhou city (2019 to 2020) with covariates extracted from remote sensing imagery and other geospatial datasets to develop an original random forest (RF) model for predicting hotspot areas (BI ≥ 5). We implemented three data resampling techniques (undersampling, oversampling, and hybrid sampling) to improve the model's performance and evaluate it using the ROC-AUC, Recall, Specificity, and G-means metrics. Finally, we generated a downscaled risk maps for BI hotspot areas at a 1000 m grid scale by applying the optimal model to fine-scale input data. Our findings indicate the following: (1) data resampling techniques significantly improved the prediction accuracy of the original RF model, demonstrating robust spatial downscaling capabilities for fine-scale grids; (2) the spatial distribution of BI hotspot areas within townships exhibits significant heterogeneity. The fine-scale risk mapping approach overcomes the limitations of previous coarse-scale risk maps and provides critical evidence for policymakers to better understand the distribution of BI hotspot areas, facilitating pixel-level spatially targeted vector control interventions in intra-urban areas.
鉴于公共卫生资源有限，生成蚊媒疾病媒介的精细尺度风险地图是指导城市地区空间靶向病媒控制干预措施的重要工具。本研究旨在利用乡镇尺度收集的常规病媒监测数据生成登革热媒介的精细尺度风险地图。我们将广州市（2019年至2020年）每月特定乡镇的布雷图指数（BI）数据与从遥感影像和其他地理空间数据集中提取的协变量相结合，开发了一个用于预测热点地区（BI≥5）的原始随机森林（RF）模型。我们实施了三种数据重采样技术（欠采样、过采样和混合采样）来提高模型性能，并使用ROC-AUC、召回率、特异性和G均值指标对其进行评估。最后，通过将最优模型应用于精细尺度输入数据，生成了1000米网格尺度下BI热点地区的降尺度风险地图。我们的研究结果表明：（1）数据重采样技术显著提高了原始RF模型的预测准确性，证明了其对精细尺度网格具有强大的空间降尺度能力；（2）乡镇内BI热点地区的空间分布呈现出显著的异质性。精细尺度风险绘图方法克服了以往粗尺度风险地图的局限性，为政策制定者更好地了解BI热点地区的分布提供了关键证据，便于在城市内部地区进行像素级空间靶向病媒控制干预。
