Spatio-temporal dynamics and risk zonation of Dengue Fever (DF), Dengue Haemorrhagic Fever (DHF), and Dengue Shock Syndeome (DSS) in Chachoengsao, Thailand | |
| Author | Phaisarn Jeefoo |
| Call Number | AIT Diss. no.RS-11-01 |
| Subject(s) | Dengue--Geographic Information Systems--Thailand--Chachoengsao Dengue--Remote sensing--Thailand--Chachoengsao |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Remote Sensing and Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Series Statement | Dissertation ; no. RS-11-01 |
| Abstract | Dengue Fever (DF), and its more severe forms, Dengue Haemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS), is the most important arthropod-transmitted viral disease affecting humans in the world today. Vector borne diseases are the most common worldwide health hazard and represent a constant and serious risk to a large part of the world’s population. In Thailand, there has been an upward trend in the incidence of dengue, with acute and severe forms of dengue virus infection, since the first dengue outbreak in 1958. In 2008, according to dengue surveillance data from Ministry of Public Health (MOPH), Thailand, the total numbers of reported cases of dengue infections in Thailand were 43,911, with 46 deaths nationwide, including 18,797 DF cases, 24,455 DHF cases, and 659 DSS cases. This investigation is aimed to contribute the concepts and methods of the innovation development and application of Geographic Information Systems (GIS) and remote sensing regarding dengue cases. A model for the spatial and temporal dynamics of dengue epidemic is also proposed in this research. Chachoengsao province, Thailand is chosen as study area. The overall objective of the study is to identify the dengue cases a based on part data and analyze probable factors responsible for dengue using GIS and remote sensing to develop a model for dengue risk zone. The specific objectives are (1) exploring and mapping of dengue risk zones using Analytical Hierarchy Process (AHP) (2) spatio-temporal diffusion pattern and risk zonation of disease and (3) an Information value (I-value) approach based analysis of land cover types and population density affecting disease. To achieve the first objective, an attempt was made to provide information to prepare dengue risk zones for decision support to mitigate the outbreak. Analytical Hierarchy Process (AHP) is used for ranking. Factor weights obtained were found to be acceptable as the consistency ratio (CR) was 0.031, which was less than 0.1. Spatial modelling included huge database about physical-environment, climatic, and demographic factors. Approximately, 74.96% people lived in very high, high, and moderate high risk zones. The most of risk zone was shown in district Mueang Chachoengsao including 142,485 or 22.34% of total population. The second objective presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999–2007) was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group), data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall), spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999–2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences.The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologic and epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. ivThe last objective was to explore the influence of physical-environmental factors and population density on dengue incidences. Remotely sensed data provided important data about physical environment and have been used for many vector borne diseases. Information value (I-value) method was utilized to derive influence for dengue earlier. Mueang Chachoengsao district was chosen to be the study area. The purpose of this objective was not only to find the effect of land cover types and population density on dengue incidences but also to find influence of these parameters in quantitative terms. I-value approach model was useful for analyzed in this study especially in deep details. SPOT 5 satellite images with 10 m resolutions was classified to extract land cover types with four-teen categories. The risk zones map was calculated using spatial analysis and processed. Recent monitoring and planning of controlling measures for dengue epidemics have become critical issue. This model offered useful information relating to the dengue incidences. The study approach is to apply GIS and Remote Sensing for dengue’s issued on health planning. The risk zones retrieved from the model may be applied along with past epidemiological information and predict the spread of the current epidemic and in order to indicate where resources might be best mobilizes. Also the approach may be applied for mapping of high-risk areas of vector-borne disease. Therefore, the use of GIS in decision support system is possible in order to enhance the awareness and warning. Public health officers may employ the model to control dengue distribution. Not only, it is applicable in epidemics, but this methodology is general and can be applied in other application fields such as dengue outbreak or other diseases during natural disasters. |
| Year | 2010 |
| Corresponding Series Added Entry | Asian Institute of Technology. Dissertation ; no. RS-11-01 |
| 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) | Souris, Marc ;Vivarad Phonekeo |
| Scholarship Donor(s) | Royal Thai Government Fellowship |
| Degree | Thesis (Ph. D.) - Asian Institute of Technology, 2011 |