Influence of socio-economic, physical and environmental factors on Dengue epidemic in Thailand using GIS-based analysis | |
| Author | Kanchana Nakhapakorn |
| Call Number | AIT Diss. no.RS-06-2 |
| Subject(s) | Dengue--Thailand--Sociological aspects Economic aspects Physiological aspects Environmental aspects |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Technical Science |
| Publisher | Asian Institute of Technology |
| Abstract | Vector-borne diseases are the most dreaded worldwide health problems causing a constant and serious risk to a large part of the world's population. Although many campaigns against it have been conducted throughout the country, Dengue Fever (DF) and Dengue Haemorrhagic Fever (DHF) is still the major health problem of Thailand. In this study, GIS based analysis was applied to evaluate the relationships between physical and climatic risk factors and the incidences of viral diseases such as DF and DHF. Remote Sensing was used to develop recent land cover map. Sukhothai province was selected as a case study. This research was presented in three objectives. First is to investigate the relationship between dengue incidence and climate factors. Second is to identify risk indicators and the last one is to explore and map of the risk zone using modeling of GIS. This research was developed the GIS-based techniques of epidemic risk modeling, with the first method demonstrating of Bayesian information value modeling. Second, multivariate regression modeling was employed to develop a dengue risk zone. Third technique was analytical hierarchy process modeling. Information Value technique was employed to develop a risk map of the area. Preliminary results demonstrated that physical factors derived from remotely sensed data could indicate variation in physical risk factors affecting DF and DHF. Results indicate that physical factors derived from remotely sensed data were indicating variation in environmental factors affecting DF/DHF. The link between land use/cover and case of incidence by information value could be understood that highest information value obtained for Built-up area and the forest area which the relationship showing negative value. This spatial factor of land use/cover is based on explicit physical processes and link between other environmental variables that have been working in the remote sensing of disease risk zone. Most of land use/land cover risk zone are show the highest area in moderately high risk class covers around 63% of the total area that about 95.3% in agriculture area and 4.6% in urban area that shown DF/DHF is recognized as a high risk in rural areas. However, Information value approach offers negative I-value in area having low risk class. This may occur in the future outbreak. The relations between climate data and cases of incidence have shown high correlation with rainfall factors in rainy season but poor correlation with temperature and relative humidity. A composite analysis of these three factors with dengue incidences was carried out using multivariate regression analysis. Three empirical models ER-1, ER-2 and ER-3 were evaluated. It was found that these three factors shown significance and can be related to the find expected number of dengue cases. The results have shown significant high coefficient of determination if applied only for the rainy season using empirical relation-2 (ER-2). These results have shown further improvement once a concept of time lag of one month was applied using the ER-3 empirical relation. ER-3 model is most suitable for the Sukhothai province in predicting possible dengue incidence with 0.81 coefficient of determination. Public Health Department can initiate their effort once the ER-3 predicts a possibility of significant high dengue incidence. This will help in focusing on the preventive measures being applied on priority in very high and high-risk zones and saving time and money. The accuracy analysis confirmed that area affected by DF and DHF are found to be located on moderately risk zones and high risk zones. The present study explores the potential of remotely sensed data and GIS in spatial analysis of factors affecting Dengue epidemic, strong spatial analysis tools of GIS. The capabilities of GIS for analyst spatial factors influencing risk zone has made it possible to apply spatial statistical analysis in Disease risk zone. Statistical analysis of data has shown a significant correlation between climate parameters and cases of incidence in affected area. Statistical analysis of data has shown a significant correlation between climate parameters and cases of incidence in affected area. From monthly data of cases of Dengue Haemorrhagic fever (DHF) and rainfall in rainy season between May - October, a pattern of DF/DHF correlated with seasons. There were many cases in the wet season. The combination of AHP method with GIS is a new trend in public health problem and a powerful combination to apply for public health monitor and control. The AHP has been introduced and applied in assessing the risk of dengue fever. Risk factors significant to this problem are identified. The results showed that there is a 85% of accuracy between dengue cases incidence and p-value of IV classes. In addition, dengue cases were 74% of accuracy AHP value model. The AHP provided a valuable to mitigate the dengue risk zone for decision support making. Therefore, for a more appropriate zonation for dengue risk zones, AHP model can be more incorporated with prevalence risk zonation. Spatial modelling. included a lot of information about physical environment factors and socio-economic factors are very useful for dengue prevention. However, there are the several factors that were not included in this model. In this study, we have investigated the influencing factors of risk zonation in dengue epidemic. We can respond faster to situation before cases occur. This research has focused on physical environment, socio-economic and climate factors. These are only two aspect of causing disease. Other three aspect causing diseases are host, environment and agent. Environmental variables were significant, but the interpretation of their effect needs to combine several types of information such as mosquito's density, container index and vector ecology. Land cover may be an important risk determinant for infection, depending on whether the landscape surrounding a person supports a large mosquito population or not, mostly by providing breeding habitats. This needs to be further investigated. The result shown that geo-informatics and environment factors should be allowed to tackle under new approach to control of the emerging viral diseases and its threat. Advances in geographic information systems, statistical methodology, and availability of high-resolution, geographically referenced health and environmental quality data have created unprecedented new opportunities to investigate environmental and other factors in explaining local geographic variations in disease. The use of GIS modelling with information value method has helped us to be more precise in defining problematic areas. For further research, the models are meant to contribute to the ongoing discussion and development of methods in the analysis of climate change, ecosystem and health relationship. Finally, future satellite imagery will dedicated to measuring for the assessment and monitoring of meteorological variables and vector-borne disease distribution. It is important to have early warning system |
| Year | 2006 |
| Type | Dissertation |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Tripathi, Nitin Kumar |
| Examination Committee(s) | Kusanagi, Michiro;Kaew Nualchawee;Preeda Parkpian;Carranza, Emmanuel John M |
| Scholarship Donor(s) | Ministry of University Affairs |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2006 |