Lourenço Pedro M, Sousa Carla A, Seixas Júlia, Lopes Pedro, Novo Maria T, Almeida A Paulo G
Departmento de Ciências e Engenharia do Ambiente, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Campus da Caparica 2829-516 Monte de Caparica, Portugal.
J Vector Ecol. 2011 Dec;36(2):279-91. doi: 10.1111/j.1948-7134.2011.00168.x.
Malaria is dependent on environmental factors and considered as potentially re-emerging in temperate regions. Remote sensing data have been used successfully for monitoring environmental conditions that influence the patterns of such arthropod vector-borne diseases. Anopheles atroparvus density data were collected from 2002 to 2005, on a bimonthly basis, at three sites in a former malarial area in Southern Portugal. The development of the Remote Vector Model (RVM) was based upon two main variables: temperature and the Normalized Differential Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite. Temperature influences the mosquito life cycle and affects its intra-annual prevalence, and MODIS NDVI was used as a proxy for suitable habitat conditions. Mosquito data were used for calibration and validation of the model. For areas with high mosquito density, the model validation demonstrated a Pearson correlation of 0.68 (p<0.05) and a modelling efficiency/Nash-Sutcliffe of 0.44 representing the model's ability to predict intra- and inter-annual vector density trends. RVM estimates the density of the former malarial vector An. atroparvus as a function of temperature and of MODIS NDVI. RVM is a satellite data-based assimilation algorithm that uses temperature fields to predict the intra- and inter-annual densities of this mosquito species using MODIS NDVI. RVM is a relevant tool for vector density estimation, contributing to the risk assessment of transmission of mosquito-borne diseases and can be part of the early warning system and contingency plans providing support to the decision making process of relevant authorities.
疟疾依赖于环境因素，在温带地区被认为有再次出现的潜在可能。遥感数据已成功用于监测影响此类节肢动物传播疾病模式的环境状况。2002年至2005年期间，每两个月在葡萄牙南部一个曾经的疟疾流行地区的三个地点收集了阿氏按蚊密度数据。远程病媒模型（RVM）的开发基于两个主要变量：温度和来自中分辨率成像光谱仪（MODIS）Terra卫星的归一化植被指数（NDVI）。温度影响蚊子的生命周期并影响其年内流行率，MODIS NDVI被用作适宜栖息地条件的替代指标。蚊子数据用于模型的校准和验证。对于蚊子密度高的地区，模型验证显示皮尔逊相关系数为0.68（p<0.05），建模效率/纳什-萨特克利夫系数为0.44，代表模型预测年内和年际病媒密度趋势的能力。RVM根据温度和MODIS NDVI估算曾经的疟疾传播媒介阿氏按蚊的密度。RVM是一种基于卫星数据的同化算法，利用温度场并结合MODIS NDVI来预测这种蚊子的年内和年际密度。RVM是病媒密度估算的相关工具，有助于蚊媒疾病传播风险评估，可成为早期预警系统和应急计划的一部分，为相关当局的决策过程提供支持。
