**地区疟疾流行区的优先排序:自组织地图(SOM)的新应用

U.S.N. Muty, N. Arora
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引用次数: 6

摘要

疟疾继续对印度东北部各邦的公共卫生构成严重威胁。**的疟疾高度流行,主要是恶性疟原虫感染。尽管政府不断努力,但仍未达到理想的控制水平。本研究描述了自组织地图(Kohonen地图)的应用,这是一种数据挖掘工具,用于该地区疟疾流行区的优先排序。从**随机抽取60所公立卫生院,采用血液年检率(ABER)、寄生虫年发病率(API)、载玻片阳性率(SPR)、恶性疟原虫年发病率(AFI)和载玻片恶性疟原虫年发病率(SFR) 6项疟疾计量指标,反映该地区疟疾传播强度。基于邻域距离的自组织地图产生了9个聚类,反映了基于疟疾流行病学强度状况的区域。这种地图将使控制措施有可能针对高风险地区,并大大提高疟疾控制方案的成本效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prioritization of Malaria endemic zones in Arunachal Pradesh: A novel application of self organizing maps (SOM)
Malaria continues to pose a serious threat to public health in NorthEastern states of India. Arunachal Pradesh is highly endemic for Malaria predominately with Plasmodium falciparium infections. Despite continuous efforts by government, a desirable level of control has not been achieved. The present study describes the application of self organizing maps (Kohonen maps), a data mining tool for prioritization of malaria endemic zones in this region. 60 PHCs (Public Health Centers) were randomly selected from Arunachal Pradesh and 6 malariometric parameters via Annual Blood Examination rate (ABER), Annual Parasite Incidence (API), Slide Positivity Rate (SPR), Annual Falciparum Incidence (AFI) and Slide Falciparum Rate (SFR) were considered which reflected the intensity of malaria transmission in this region. Self Organizing Maps yielded 9 clusters based on neighborhood distance, which reflects about zones based on status of intensity of malaria epidemiology. Such maps would make it possible to target control measures at high-risk areas and greatly increase the cost efficiency of malaria control programmes.
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