Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA.
Sao Paulo State University, Presidente Prudente.
Geospat Health. 2022 Jun 8;17(1). doi: 10.4081/gh.2022.1095.
Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk.
内脏利什曼病（VL）是一种由分布广泛的沙蝇卢氏利什曼原虫传播的被忽视的热带病。尽管努力加强国家控制计划，但巴西的发病率和地理分布的减少尚未成功；VL 实际上正在新的城市化地区扩大其范围。用于监测和应对系统的生态位模型（ENM）可以通过绘制风险区域和阐明生态流行病学风险因素，使 VL 控制更有效。使用每月 WorldClim 2.0 数据（30 年气候正常，1 公里空间分辨率）和每月土壤水分主动被动（SMAP）卫星 L4 土壤水分数据生成 VL 和 Lu. longipalpis 的 ENM。为了获得每个月的第一天和第 15 天（0:00-3:00 小时）的第一张图像，获取了 SMAP L4 Global 3 小时 9 公里 EASE-Grid 表面和根区土壤水分地球物理数据 V004。使用 MaxEnt 软件开发了 ENM，以根据最大熵算法生成风险图。刀切程序用于确定每个变量对模型性能的贡献。使用 ArcGIS 10.6 生成 ENM 分布图，使用三个最有意义的组件。与基于温度和降水数据或水预算的 WorldClim 2.0 模型相比，使用 SMAP 观察到 VL 和矢量分布的相似模式。结果表明，可以使用 SMAP 对土壤湿度进行直接的地球观测卫星测量来代替从经典温度和降水气候站数据计算的模型来评估 VL 风险。
