利用地形模型检测疟疾病媒繁殖栖息地
Detection of Malaria Vector Breeding Habitats using Topographic Models
AI总结：
本研究提出基于高分辨率DEM数据的地形模型预测疟疾病媒繁殖水体位置，在加纳奥布阿西地区验证，最佳模型显著优于早期方法并具鲁棒性。
摘要：
处理作为疟疾病媒繁殖场所的积水水体是大多数消除疟疾运动中的基本步骤。然而，在大面积区域内识别此类水体成本高昂、劳动密集且耗时，因此在资源有限的国家中具有挑战性。能够高效定位水体的实用模型可以通过大幅减少现场工作人员需要扫描的区域来有针对性地分配有限资源。为此，我们提出了一种基于易于获取的全球高分辨率DEM数据的实用地形模型，用于预测潜在病媒繁殖水体的位置。我们对加纳奥布阿西地区进行了调查，以评估各种地形特征对不同类型水体的影响，并揭示显著影响水生栖息地形成的特征。我们进一步评估了多种模型的有效性。我们的最佳模型显著优于早期利用地形变量检测小型水体（甚至包括利用额外卫星影像数据的模型）的尝试，并在不同设置下表现出鲁棒性。
英文摘要：
Treatment of stagnant water bodies that act as a breeding site for malarial vectors is a fundamental step in most malaria elimination campaigns. However, identification of such water bodies over large areas is expensive, labour-intensive and time-consuming and hence, challenging in countries with limited resources. Practical models that can efficiently locate water bodies can target the limited resources by greatly reducing the area that needs to be scanned by the field workers. To this end, we propose a practical topographic model based on easily available, global, high-resolution DEM data to predict locations of potential vector-breeding water sites. We surveyed the Obuasi region of Ghana to assess the impact of various topographic features on different types of water bodies and uncover the features that significantly influence the formation of aquatic habitats. We further evaluate the effectiveness of multiple models. Our best model significantly outperforms earlier attempts that employ topographic variables for detection of small water sites, even the ones that utilize additional satellite imagery data and demonstrates robustness across different settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
