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| 008 | 040820s2003 th u m rtt 00| a1eng d | ||
| 035 | _a.b11937324 | ||
| 099 | 9 | _aAIT Thesis no. SR-03-08 | |
| 100 | 1 | _aNiu, Shulian | |
| 245 | 1 | 0 |
_aMalaria distribution and prediction based on GIS and RS : _ba case study in Tak province |
| 260 |
_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2003 |
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| 300 | _a58 leaves | ||
| 490 | 1 |
_aThesis ; _vno. SR-03-08 |
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| 500 | _aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science, School of Advanced Technologies | ||
| 502 | _aThesis (M.Sc.) - Asian Institute of Technology, 2003 | ||
| 520 | _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. | ||
| 650 | 0 |
_aMalaria _zThailand _zTak |
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| 700 | 1 |
_aKusanagi, Michiro, _eChairperson |
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| 700 | 1 |
_aSamarakoon, Lal, _eExamination Committee |
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| 700 | 0 |
_aSrisaang Kaojarem, _eExamination committee |
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| 700 | 1 |
_aSouris, Marc, _eExamination committee |
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| 710 | 2 |
_aGovernment of Japan, _eScholarship donor |
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| 810 | 2 |
_aAsian Institute of Technology. _tThesis ; _vno. SR-03-08 |
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| 856 |
_3Full-Text _u http://203.159.5.9/ait-thesis/detail.php?q=B08863 |
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