| Author | Sravan, Sajja |
| Call Number | AIT Thesis no.RS-16-06 |
| Subject(s) | Diseases and hygiene--Phayao
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| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Remote Sensing and Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. RS-16-06 |
| Abstract | Intensive use of RS & GIS in health sector had given an opportunity for healthcare authorities to identify the risk zones for different diseases through which the services can be rendered more effectively, but this work is still under tremendous pressure especially in developing countries and countries with large population, due to the limitations in data availability. When considering low mortality rate disease with high morbidity are further neglected. For years healthcare authorities responded to disease outbreak by increasing healthcare facilities. Various researches have shown that increasing healthcare facilities alone cannot help in improvising service to people, but also service to the most needed by finding out risk zones to a very small scale can bring a drastic change in allocation of services by healthcare sector. So rendering services more effectively is very important. Diarrhea, food poisoning, pneumonia, influenza, and chickenpox are the endemic diseases not only seen in Thailand but also considered as dangerous throughout the world. Diarrhea and food poisoning are food and water born disease which show high incidence of cases in developing nations due to low sanitary conditions. Pneumonia and influenza are the respiratory diseases and chickenpox is an infectious disease. This study concentrates on identifying climatic influence, spatial patterns, and hotspots for the years 1997 - 2014 considered study area is Mueang Phayao district, capital of Phayao province. Disease, climatic, demographic and GIS data is acquired and prepared for analysis. Disease incidence rate is calculated for each village and empirical Bayes smoothing is used to eliminate outliers from data and kernel density is used to generate choropleth maps using disease morbidity rate. Relation between meteorological factors and disease cases is generated. Global and local SAA are used to determine spatial patterns and hotspots respectively. Techniques used for this process are global Moran's Iand LISA respectively. For the year 2015 disease cases are considering in Phayao province as a whole and generate spatial patterns and hotspots as mentioned above. Risk modeling is done considering factors such as land - use, distance from water bodies, rainfall, elevation and population factors using Analytical Hierarchical Process (AHP). The risk maps can be verified by overlaying disease hotspots and checking their existence. The models can be used by health officers to identify risk areas for each disease and render their services more effectively and taking preventive measures. Results published in the web is great help in reaching to many people, this is done by sharing, processing and editing by GeoServer and create a local web server for data publishing by Apache XAMPP. |
| Year | 2016 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. RS-16-06 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Tripathi, Nitin Kumar |
| Examination Committee(s) | Sarawut Ninsawat;Souris, Marc;Phaisarn Jeefoo |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2016 |