Spatial-temporal distribution of Anopheles larval habitats in Uganda using GIS/remote sensing technologies.
Spatial-temporal distribution of Anopheles larval habitats in Uganda using GIS/remote sensing technologies.
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DOI：
10.1186/s12936-018-2567-z
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发表时间：
2018-11-12
期刊：
影响因子：
3
通讯作者：
Novak RJ
中科院分区：
文献类型：
--
作者：
Tokarz R;Novak RJ
在人类健康和舒适方面，按蚊给非洲人口带来了巨大的负担。尤其是乌干达，它号称是世界上疟疾传播率最高的国家之一，其所有人口都面临感染的风险。尽管这些蚊子给该国造成了巨大的负担，但在病媒控制一级直接应对这一问题的方案很少，更少的方案侧重于病媒最脆弱的幼虫阶段。这项研究利用遥感技术和空间自相关模型来确定乌干达农村社区中最多产按蚊幼虫栖息地的优先次序，以便进行控制。在乌干达东部的Papoli教区，制定并实施了为期4个月的社区蚊子监测方案。每天，一个训练有素的野外团队在该社区人口密集的地区对按蚊的幼虫栖息地进行采样。每天确定和绘制栖息地及其生产力的空间图。日产量被合并并显示为每周栖息地时间序列。使用Global和Anselin的Local Moran‘s I统计量进行了额外的空间分析，以评估栖息地的空间自相关性。开发了空间模型以确定高度重要的生境，并规定了这些生境用于控制幼虫的优先次序。每周的时间序列模型确定了每个栖息地的位置和生产力，而Local Moran I的聚类图确定了具有统计意义的集群(集群：高)和离群值(高离群值)，然后将其解释为控制优先级。模型以时间格式缝合在一起，以直观地展示在整个研究期间静态意义重大的高优先级栖息地的空间变化。研究结果表明，生产性生境的空间结果可以通过初始生境模型和由此产生的时间序列输出而明显地显现出来。然而，蚊子控制资源往往是有限的，正是在这一点上，当地莫兰的I统计数据证明了其价值。将重点放在被确定为集群的生境：高和高离群值输出允许识别最有影响的幼虫生境。利用这种方法控制疟疾，可以实时、以社区为导向优化控制资源，并为未来的控制实践提供一个框架。
Anopheles mosquitoes impose an immense burden on the African population in terms of both human health and comfort. Uganda, in particular, boasts one of the highest malaria transmission rates in the world and its entire population is at risk for infection. Despite the immense burden these mosquitoes pose on the country, very few programmes exist that directly combat the issue at the vector control level and even fewer programmes focus on the vector in its most vulnerable juvenile stages. This study utilizes remote sensing techniques and spatial autocorrelation models to identify and prioritize the most prolific Anopheline larval habitats for control purposes in a rural community in Uganda. A community-based mosquito surveillance programme was developed and implemented in Papoli Parish in Eastern Uganda over a 4-month period. Each day, a trained field team sampled the larval habitats of Anopheles mosquitoes within the population-dense areas of the community. Habitats and their productivity were identified and plotted spatially on a daily basis. Daily output was combined and displayed as a weekly habitat time-series. Additional spatial analysis was conducted using the Global and Anselin’s Local Moran’s I statistic to assess habitat spatial autocorrelation. Spatial models were developed to identify highly significant habitats and dictated the priority of these habitats for larval control purposes. Weekly time-series models identified the locations and productivity of each habitat, while Local Moran’s I cluster maps identified statistically significant clusters (Cluster: High) and outliers (High Outlier) that were then interpreted for control priority. Models were stitched together in a temporal format to visually demonstrate the spatial shift of statically significant, high priority habitats over the entire study period. The findings show that the spatial outcomes of productive habitats can be made starkly apparent through initial habitat modelling and resulting time-series output. However, mosquito control resources are often limited and it is at this point that the Local Moran’s I statistics demonstrates its value. Focusing on habitats identified as Cluster: High and High Outlier outputs allow for the identification of the most influential larval habitats. Utilizing this method for malaria control allows for the optimization of control resources in a real time, community driven, fashion, as well as providing a framework for future control practices.
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影响因子：
2.1
作者：
BEIER, JC;PERKINS, PV;KOECH, DK
通讯作者：
KOECH, DK
影响因子：
3.6
作者：
ANSELIN, L
通讯作者：
ANSELIN, L
DOI：
10.4269/ajtmh.2002.67.32
发表时间：
2002-07-01
影响因子：
3.3
作者：
Minakawa, N;Seda, P;Yan, GY
通讯作者：
Yan, GY
影响因子：
3.1
作者：
Lehmann, T;Licht, M;Hawley, WA
通讯作者：
Hawley, WA
影响因子：
2.1
作者：
DAY, JF;CURTIS, GA;EDMAN, JD
通讯作者：
EDMAN, JD