02619nas|a2200253 i 450000500170000000800410001703500150005810000170007324500920009026000670018230000140024949000270026350001350029050200570042552015200048265000270200270000360202970000440206570000460210970000410215571000440219681000590224085600660229920260818153941.0040820s2003    th  u m rtt  00|  a1eng d  a.b119373241 aNiu, Shulian10aMalaria distribution and prediction based on GIS and RS :ba case study in Tak province  aPathum Thani, Thailand :bAsian Institute of Technology,c2003  a58 leaves1 aThesis ;vno. SR-03-08  aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science, School of Advanced Technologies  aThesis (M.Sc.) - Asian Institute of Technology, 2003  aMalaria is one of the mosquito-borne viral diseases, which is a serious infection transmitted by female Anopheles. There are various forms of infectious diseases caused by mosquitoes but they vary depending on climate and land cover patterns. In Thailand, malaria still remains a public health problem in some provinces due to its high fatality rate especially in northern Thailand. In this study, the whole decade data of malaria cases are analyzed to identify the highest malaria incidence province-Tak province, then climatic data (rainfall, temperature, humidity), demographic data (age distribution, education status) and physical environmental data (Landsat satellite image) in the highest malaria incidence province are collected to calculate the correlations with malaria cases. The results show that the malaria disease has high correlation with rainfall and humidity, on the contrary to the low correlation with temperature. Meanwhile, malaria disease has the highest positive correlation with age distribution in 0-4 years, and negative in 35-39 years range. Education status has low correlation with malaria disease based on the methodology. In addition, agriculture and forestry led environment are found to be the major land covers in the higher malaria incidence area based from the classified satellite image. Finally, multiple regression analyses based on rainfall and humidity in different time series are analyzed to develop equations that could predict the probable incidence of malaria disease. 0aMalariazThailandzTak1 aKusanagi, Michiro,eChairperson1 aSamarakoon, Lal,eExamination Committee0 aSrisaang Kaojarem,eExamination committee1 aSouris, Marc,eExamination committee2 aGovernment of Japan,eScholarship donor2 aAsian Institute of Technology.tThesis ;vno. SR-03-08  3Full-Textu http://203.159.5.9/ait-thesis/detail.php?q=B08863