AI SearchAnopheles mosquito density predictive model based on remotely sensed data
Call Number: AIT Diss. no.RS-09-01 Material type:
SeriesSeries: Asian Institute of Technology. Dissertation ; no. RS-09-01Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2009Description: 167 p. : illSubject(s): Online resources: Dissertation note: Thesis (Ph.D.) - Asian Institute of Technology, 2009 Summary: Malaria cases and its consequent deaths have been predominant unsolved public health issues in Thailand for decades, despite cases have fallen gradually since 1999. Malaria transmits through three possible mediums; malaria parasite, human hosts, and Anopheles mosquito. One possible way to solve the malaria problem is to have intervention on any of these mediums. This study focuses on Anopheles mosquito medium, a part of the malaria transmission cycle. As the malaria control methods depend on many setting-specific factors such as endemic, vector species and behavior, seasonality, disease patterns, health service factors and more, which they have not been distributed equally in spatial, therefore the accuracy of these predicted information at timely manner are necessary requirements for effective malaria control planning and preparations. Thus, increasing of spatial accuracy and information updates on the vector density are the main issues for the malaria control. In order to support these requirements, Geo-informatics technology is used to develop the model for predicting Anopheles mosquitoes, which is called "Anopheles Mosquito Density Predictive Model (AMDP model)". This model was developed by based on reality-based and knowledge-based systems. Remote sensing, Geographic Information Systems (GIS), Global Positioning System (GPS), and statistical models were integrated to develop the model. It is found that NDVI values that are higher than 0.501, temperature values with the range of 25-29°C, relative humidity values with the range of 81-85%, and deciduous forest land cover are the best predictors of the Anopheles mosquito density classes in wet season, while NDVI values that are higher than 0.501, temperature values with the range of 25-29°C, deciduous forest land cover, and elevation 400-700 meters interval are the best predictors for the Anopheles mosquito density classes in dry season. AMDP model was able to predict correctly 79.7% and 73.8% in wet and dry seasons. This model has passed the model validation and model credibility procedures. The results indicate that the model could be applied for prediction of the Anopheles mosquito density in other areas, malaria cases and a tool for decision making system for malaria control planning
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A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Technical Science
Thesis (Ph.D.) - Asian Institute of Technology, 2009
Malaria cases and its consequent deaths have been predominant unsolved public health issues in Thailand for decades, despite cases have fallen gradually since 1999. Malaria transmits through three possible mediums; malaria parasite, human hosts, and Anopheles mosquito. One possible way to solve the malaria problem is to have intervention on any of these mediums. This study focuses on Anopheles mosquito medium, a part of the malaria transmission cycle. As the malaria control methods depend on many setting-specific factors such as endemic, vector species and behavior, seasonality, disease patterns, health service factors and more, which they have not been distributed equally in spatial, therefore the accuracy of these predicted information at timely manner are necessary requirements for effective malaria control planning and preparations. Thus, increasing of spatial accuracy and information updates on the vector density are the main issues for the malaria control. In order to support these requirements, Geo-informatics technology is used to develop the model for predicting Anopheles mosquitoes, which is called "Anopheles Mosquito Density Predictive Model (AMDP model)". This model was developed by based on reality-based and knowledge-based systems. Remote sensing, Geographic Information Systems (GIS), Global Positioning System (GPS), and statistical models were integrated to develop the model. It is found that NDVI values that are higher than 0.501, temperature values with the range of 25-29°C, relative humidity values with the range of 81-85%, and deciduous forest land cover are the best predictors of the Anopheles mosquito density classes in wet season, while NDVI values that are higher than 0.501, temperature values with the range of 25-29°C, deciduous forest land cover, and elevation 400-700 meters interval are the best predictors for the Anopheles mosquito density classes in dry season. AMDP model was able to predict correctly 79.7% and 73.8% in wet and dry seasons. This model has passed the model validation and model credibility procedures. The results indicate that the model could be applied for prediction of the Anopheles mosquito density in other areas, malaria cases and a tool for decision making system for malaria control planning
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