The importance of appropriate temporal and spatial scales for dengue fever control and management
The importance of appropriate temporal and spatial scales for dengue fever control and management
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DOI：
10.1016/j.scitotenv.2012.05.001
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发表时间：
2012-07-15
影响因子：
9.8
通讯作者：
Kumar, Lalit
中科院分区：
文献类型：
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作者：
Khormi, Liassan M.;Kumar, Lalit
重要的是要有适当的模式，监测和控制蚊媒疾病，如登革热（OF）。这些模型需要以适当的时间和空间尺度为基础。本研究的目的是说明不同的时间和空间尺度对DF控制决策的影响。我们应用Getis-Ord Gi* 统计在不同的时间和空间尺度，以检查在这些尺度上的空间集群的地方水平，以确定和可视化的成年雌性伊蚊的数量是极端的和地理上同质的地区。建模的热点区域是不同的，这取决于它们是基于每周、每月还是每年的汇总数据建模的。当使用不同的空间尺度进行建模时，发现了类似的结果，不同的尺度给出了不同的热点区域。2006年，确定的最高风险地区（18个地区）大多位于中心地区，与平均五年期模型确定的最高风险地区（19个地区）相比，相似率很高（95%）。适当的时间和空间尺度的知识可以提供一个机会，以指定的健康负担的OF及其载体内的热点，以及设置一个平台，可以帮助追求进一步调查相关因素负责增加疾病风险的基础上不同的时间和空间尺度。(C)2012爱思唯尔有限公司版权所有。
It is important to have appropriate models for the surveillance and control of mosquito-borne diseases, such as dengue fever (OF). These models need to be based on appropriate temporal and spatial scales. The aim of this study was to illustrate the impact of different temporal and spatial scales on DF control decisions. We applied the Getis-Ord Gi* statistic at different temporal and spatial scales to examine the local level of spatial clusters at these scales in order to identify and visualize areas where numbers of adult female Aedes mosquitoes were extreme and geographically homogenous. The modeled hotspot areas were different, depending on whether they were modeled on weekly, monthly or yearly aggregated data. A similar result was found when using different spatial scales for modeling, with different scales giving different hotspot regions. For 2006, the highest risk areas (18 districts) were mostly identified in the central districts with a high rate of similarity (95%) compared to the highest risk areas (19) identified in the averaged five-year period model. Knowledge of appropriate temporal and spatial scales can provide an opportunity to specify the health burden of OF and its vector within the hotspots, as well as set a platform that can help to pursue further investigations into associated factors responsible for increased disease risk based on different temporal and spatial scales. (C) 2012 Elsevier B.V. All rights reserved.