The development of high resolution maps of tsetse abundance to guide interventions against human African trypanosomiasis in northern Uganda.
The development of high resolution maps of tsetse abundance to guide interventions against human African trypanosomiasis in northern Uganda.
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DOI：
10.1186/s13071-018-2922-5
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发表时间：
2018-06-08
影响因子：
3.2
通讯作者：
Torr SJ
中科院分区：
文献类型：
--
作者：
Stanton MC;Esterhuizen J;Tirados I;Betts H;Torr SJ
病媒控制正在成为全球控制冈比亚昏睡病（非洲锥虫病）努力的一个重要组成部分。部署经杀虫剂处理的目标（“微小目标”）以吸引和杀死布氏冈比亚锥虫的媒介-河上采采蝇，已证明特别具有成本效益。随着这种病媒控制方法继续在更大的地区实施，了解采采蝇的数量以指导“微小目标”的部署将具有越来越大的价值。在本文中，我们使用地统计建模框架来生成两种情况下的舌蝇丰度估计地图：（i）当当地河流网络有准确数据时;（ii）当河流信息稀疏时。采采蝇丰度数据来自2010年在乌干达北方进行的干预前调查。从数字化地图或从30米分辨率数字高程模型（DEM）数据获得的河网数据作为地面实况数据的代理。其他环境变量来自公开可用的分辨率遥感数据（例如陆地卫星，30 m分辨率）。采用综合嵌套拉普拉斯近似法，将零膨胀负二项地质统计模型与丰度数据拟合，并绘制了舌蝇丰度估计图。限制分析陷阱位于100米以内的任何河流，确定了河流的长度和周围地区的最低土壤/植被含水量和每日苍蝇捕获量之间的正相关性，而负相关性与海拔和距离的河流。所得到的模型可以准确地区分具有高和低苍蝇捕获量（例如5只< 5 or >苍蝇/天）的诱捕器，其中ROC-AUC（接收器操作特征-曲线下面积）大于0.9。虽然没有很好地近似使用DEM数据的河流的精确过程中，使用DEM派生的河流数据拟合的模型进行类似的那些纳入更准确的本地河流信息。这些模型现在可用于协助乌干达北方舌蝇控制行动的设计、实施和监测，并可进一步用作在其他地区进行类似研究的框架，在这些地区，Glossina fuscipes fuscipes传播冈比亚昏睡病。本文的在线版本（10.1186/s13071-018-2922-5）包含补充材料，可供授权用户使用。
Vector control is emerging as an important component of global efforts to control Gambian sleeping sickness (human African trypanosomiasis, HAT). The deployment of insecticide-treated targets (“Tiny Targets”) to attract and kill riverine tsetse, the vectors of Trypanosoma brucei gambiense, has been shown to be particularly cost-effective. As this method of vector control continues to be implemented across larger areas, knowledge of the abundance of tsetse to guide the deployment of “Tiny Targets” will be of increasing value. In this paper, we use a geostatistical modelling framework to produce maps of estimated tsetse abundance under two scenarios: (i) when accurate data on the local river network are available; and (ii) when river information is sparse. Tsetse abundance data were obtained from a pre-intervention survey conducted in northern Uganda in 2010. River network data obtained from either digitised maps or derived from 30 m resolution digital elevation model (DEM) data as a proxy for ground truth data. Other environmental variables were derived from publicly-available resolution remotely sensed data (e.g. Landsat, 30 m resolution). Zero-inflated negative binomial geostatistical models were fitted to the abundance data using an integrated nested Laplace approximation approach, and maps of estimated tsetse abundance were produced. Restricting the analysis to traps located within 100 m of any river, positive associations were identified between the length of river and the minimum soil/vegetation moisture content of the surrounding area and daily fly catches, whereas negative associations were identified with elevation and distance to the river. The resulting models could accurately distinguish between traps with high and low fly catches (e.g. < 5 or > 5 flies/day), with a ROC-AUC (receiver-operating characteristic - area under the curve) greater than 0.9. Whilst the precise course of the river was not well approximated using the DEM data, the models fitted using DEM-derived river data performed similarly to those that incorporated the more accurate local river information. These models can now be used to assist in the design, implementation and monitoring of tsetse control operations in northern Uganda and further can be used as a framework by which to undertake similar studies in other areas where Glossina fuscipes fuscipes spreads Gambian sleeping sickness. The online version of this article (10.1186/s13071-018-2922-5) contains supplementary material, which is available to authorized users.
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影响因子：
2.6
作者：
Ducheyne, E.;Mweempwa, C.;De Pus, C.;Vernieuwe, H.;De Deken, R.;Hendrickx, G.;Van den Bossche, P.
通讯作者：
Van den Bossche, P.
从陆地卫星图像中提取河流和湖泊的自动化方法
影响因子：
5
作者：
Jiang, Hao;Feng, Min;Xiao, Tong
通讯作者：
Xiao, Tong
影响因子：
3.8
作者：
Lehane M;Alfaroukh I;Bucheton B;Camara M;Harris A;Kaba D;Lumbala C;Peka M;Rayaisse JB;Waiswa C;Solano P;Torr S
通讯作者：
Torr S
DOI：
10.1073/pnas.1407773111
发表时间：
2014-07-15
影响因子：
11.1
作者：
Dicko, Ahmadou H.;Lancelot, Renaud;Bouyer, Jeremy
通讯作者：
Bouyer, Jeremy
影响因子：
4.8
作者：
Kitron, U;Otieno, LH;Cook, E
通讯作者：
Cook, E