Anno Sumiko, Imaoka Keiji, Tadono Takeo, Igarashi Tamotsu, Sivaganesh Subramaniam, Kannathasan Selvam, Kumaran Vaithehi, Surendran Sinnathamby Noble
Shibaura Institute of Technology, Tokyo.
Geospat Health. 2015 Nov 26;10(2):376. doi: 10.4081/gh.2015.376.
The aim of the present study was to identify geographical areas and time periods of potential clusters of dengue cases based on ecological, socio-economic and demographic factors in northern Sri Lanka from January 2010 to December 2013. Remote sensing (RS) was used to develop an index comprising rainfall, humidity and temperature data. Remote sensing data gathered by the AVNIR-2 instrument onboard the ALOS satellite were used to detect urbanisation, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analysed RS data and databases were integrated into a geographical information system (GIS) enabling space-time clustering analysis. Our results indicate that increases in the number of combinations of ecological, socio-economic and demographic factors that are present or above the average contribute to significantly high rates of space-time dengue clusters. The spatio-temporal association that consolidates the two kinds of associations into one can ensure a more stable model for forecasting. An integrated spatiotemporal prediction model at a smaller level using ecological, socioeconomic and demographic factors could lead to substantial improvements in dengue control and prevention by allocating the right resources to the appropriate places at the right time.
本研究的目的是根据2010年1月至2013年12月斯里兰卡北部的生态、社会经济和人口因素，确定登革热病例潜在聚集的地理区域和时间段。利用遥感（RS）技术开发了一个包含降雨、湿度和温度数据的指数。利用ALOS卫星上的AVNIR-2仪器收集的遥感数据来检测城市化，并使用数字土地覆盖图来提取土地覆盖信息。通过机构和现有数据库收集了有关相关因素和登革热疫情的其他数据。将分析后的遥感数据和数据库整合到地理信息系统（GIS）中，进行时空聚类分析。我们的结果表明，存在或高于平均水平的生态、社会经济和人口因素组合数量的增加，会导致时空登革热聚集率显著升高。将两种关联整合为一种的时空关联可以确保更稳定的预测模型。利用生态、社会经济和人口因素在较小尺度上建立综合时空预测模型，通过在正确的时间将正确的资源分配到合适的地点，可以显著改善登革热的控制和预防。
