A high-resolution geospatial surveillance-response system for malaria elimination in Solomon Islands and Vanuatu
A high-resolution geospatial surveillance-response system for malaria elimination in Solomon Islands and Vanuatu
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DOI：
10.1186/1475-2875-12-108
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发表时间：
2013-03-21
期刊：
影响因子：
3
通讯作者：
Clements, Archie C. A.
中科院分区：
文献类型：
--
作者：
Kelly, Gerard C.;Hale, Erick;Clements, Archie C. A.
背景资料：在地理信息系统内开发了一个高分辨率监测反应系统，以支持在太平洋消除疟疾。本文探讨了应用GIS为基础的空间决策支持系统（SDSS），自动定位和地图的分布，确诊疟疾病例，快速分类活动传播疫源地，并指导有针对性的反应，在消除zones.Methods：定制的基于SDSS的监测响应系统开发的三个消除省份的伊莎贝尔和Temotu，所罗门群岛和塔菲亚，瓦努阿图。作为2011年全年例行业务的一部分，确诊疟疾病例在诊断后向省级疟疾办公室报告，并更新到各自的国家疾病统计系统。使用现有的地理侦察数据，在SDSS中按家庭自动绘制病例图。将GIS查询集成到SDSS框架中，以根据病例的时空分布自动分类和绘制传播疫源地，突出显示当前关注区（AOI）区域，以进行针对特定疫源地的有针对性的应对，并提取支持性的家庭和人口数据。运行GIS模拟，以检测2011年在每个消除省份触发的AOI，并进行敏感性分析，以计算不同地理半径的AOI区域中突出显示的阳性病例、家庭和人口的比例。整个2011年，使用SDSS报告和绘制了总共183例确诊病例，并用于描述90名目标人群中的传播情况，354.每个省的SDSS也自动生成AOI区域，以确定进行响应的地理区域。82.5%的确诊病例在家庭一级自动进行了地理参照和绘图，其余病例100%在村庄一级进行了地理参照。AOI分析的数据表明每个省的进展处于不同阶段，突出了在考虑到案件的时空性质以及各方案的后勤和财政限制的情况下，实施监测-反应战略的业务影响。为指导太平洋岛屿消灭疟疾而开发的地理空间系统展示了高分辨率SDSS的应用，在这方面，需要采取一种基于基础的方法，以支持监测-应对的关键要素，包括了解目标地区内的流行病学变化，实施适当的、针对特定重点疾病的有针对性的应对措施，并考虑后勤制约因素和成本。
Background: A high-resolution surveillance-response system has been developed within a geographic information system (GIS) to support malaria elimination in the Pacific. This paper examines the application of a GIS-based spatial decision support system (SDSS) to automatically locate and map the distribution of confirmed malaria cases, rapidly classify active transmission foci, and guide targeted responses in elimination zones.Methods: Customized SDSS-based surveillance-response systems were developed in the three elimination provinces of Isabel and Temotu, Solomon Islands and Tafea, Vanuatu. Confirmed malaria cases were reported to provincial malaria offices upon diagnosis and updated into the respective SDSS as part of routine operations throughout 2011. Cases were automatically mapped by household within the SDSS using existing geographical reconnaissance (GR) data. GIS queries were integrated into the SDSS-framework to automatically classify and map transmission foci based on the spatiotemporal distribution of cases, highlight current areas of interest (AOI) regions to conduct foci-specific targeted response, and extract supporting household and population data. GIS simulations were run to detect AOIs triggered throughout 2011 in each elimination province and conduct a sensitivity analysis to calculate the proportion of positive cases, households and population highlighted in AOI regions of a varying geographic radius.Results: A total of 183 confirmed cases were reported and mapped using the SDSS throughout 2011 and used to describe transmission within a target population of 90,354. Automatic AOI regions were also generated within each provincial SDSS identifying geographic areas to conduct response. 82.5% of confirmed cases were automatically geo-referenced and mapped at the household level, with 100% of remaining cases geo-referenced at a village level. Data from the AOI analysis indicated different stages of progress in each province, highlighting operational implications with regards to strategies for implementing surveillance-response in consideration of the spatiotemporal nature of cases as well as logistical and financial constraints of the respective programmes.Conclusions: Geospatial systems developed to guide Pacific Island malaria elimination demonstrate the application of a high resolution SDSS-based approach to support key elements of surveillance-response including understanding epidemiological variation within target areas, implementing appropriate foci-specific targeted response, and consideration of logistical constraints and costs.