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The geographical and seasonal distributions of many infectious diseases are linked to climate, therefore the possibility of using seasonal climate forecasts as predictive indicators in disease outbreak (WHO, 2004). In addition, convincing evidence that anthropogenic influences are causing the world's climate to change has provided an added incentive to improve understanding of climate-disease interactions.
The factors of climate variability, minimum temperature, maximum temperature, humidity and rainfall are major causes of seasonal disease that has impact to socio-economic and public health. Therefore, it needs to become interested in possible linked between climate variability and cyclic epidemics in areas where there climate connections. There may be great potential in applying the rapidly advancing science of seasonal, or El Niño, forecasting to disease risk and it is hoped that the health sector will be able to make good use of this information in health service planning (Kovats, 2000).
Commons indicator of meteorological data use annual temperature, humidity and rainfall, which is major determinant of the disease outbreak, but its seasonal variability is very high and there are some limited coverage and/or resolution in space and time. Analysis of climate variability was conducted by both meteorological data and disease cases data from population at village level were obtained by Chiang Mai Provincial Public Health Office (CMPHO), Thailand. The relationship between climate and disease pattern are well established with many occurring during seasonal or erupting from unseasonable, flood or draught condition.
The development of geographic information systems (GIS) over last 30 years has provided a more powerful and rapid ability to investigate spatial patterns of diseases and processes. This is referred to and more useful in epidemiologic investigations and also disease surveillance including policy relevant issues such as health services and planning (Matthews, 1990).
GIS is an information system with a geographical variable which is decision support tool for planning, monitoring and analyzing the spatial data. Application of GIS in spatial epidemiology (i.e. probability mapping, spatial interpolation, spatial autocorrelation analysis and space-time analysis) can be simple to visualize and analyze the geographic distribution of diseases through time (patterns, trends and relationships) (Ruankaew, 2005) that would be more benefit understand the spatial spread or diffusion of disease outbreaks (i.e. the occurrence of a large number of disease cases in a restricted geographical area over a short period of time (WHO, 2009)). Kulldorff et al. (2005) developed new spatial epidemiological approach, called the "space time permutation scan statistic", for early disease outbreak detection. The approach was integrated in the New York City Emergency Department syndromic surveillance system that was found for detecting and monitoring outbreaks of water-borne diseases.
GIS system is a decision support tool for planning, monitoring and analyzing the spatial data. The logic of using GIS to study disease or health care is derived from appreciation of factors causing non-uniformity of disease distribution (Mayer, 1983). These factors include physical and environmental factors; social, economic and cultural factors; and climate factors, all of these factors may have spatial distributions influencing the extent and intensity of a particular disease.
Although the attempt to employ the spatial epidemiological approaches and GIS for disease surveillance has become the important tool in epidemiological studies of Thailand, few of many epidemiology studies lauding the applications of these approaches and GIS emerge from researchers dealing with Thai conditions. The related studies focused only on spatial-statistical models for disease mapping and exploring the geographic distribution patterns of the vector-borne diseases at regional levels (i.e. sub-district, district and province.
Therefore, the aim of this study is applying the GIS approach to visualize and analyze the geographic distribution of diseases through time (patterns, trends and relationships) that would be more benefit understands the spatial spread or diffusion of disease outbreaks related with climate factors. Forecasting of Climate variability which are connection to infectious disease outbreak will showed the results of association of climate factors and seasonal disease epidemic outbreak pattern in the future. The statistic of this study is the fuzzy logic regressions by generating model for predicting morbidity disease outbreak.
The northern Thailand, Chiang Mai province has been a dominant area of endemic diseases such as diarrhea, food poisoning, pneumonia, dengue fever etc. (CMPHO, 2006). The geographic distribution of diseases, marks high variability at district, sub-district and village levels, relates to socio-demographic, environment and climate factors. A better understanding of the geographic phenomena (i.e. spatial patterns, hotspots and diffusion) of these disease outbreaks is central to the design of prevention and control strategies for public health officers who work in this area. For diseases surveillance of Chiang Mai, data about patients with 81 diseases were obtained by the Chiang Mai Provincial Public Health Office (CMPHO) for supporting their surveillance system. The data contains the daily patients at village level, including the patient age, gender, address, date of sick, village code etc. The data can be useful for the surveillance system and the spatial epidemiological studies at village level.
This study is focusing to apply the spatial pattern climatology and epidemiology approach for studying seasonal epidemic, disease surveillance in Chiang Mai. The specific objectives are following:
To transform the incidence database of disease pattern by applying statistical techniques to indicate disease mapping and cluster detection.
To analyze the relationship between climate variability and epidemic disease.
To verify the new technique integrate GIS using the fuzzy logic for epidemic risk zone map.
1.3 Structure of the dissertation
The dissertation contains eight chapters as follows:
Chapter 1 is an introduction which discusses the requirement of the spatial epidemiological approaches and GIS in the epidemiological studies of Thailand in addition to objectives of the study. Chapter 2 covers the literature reviews on research related to the objectives. This chapter reviews previous works on the spatial epidemiology approaches and GIS in the health applications. The descriptions of the study area and data used are depicted in Chapter 3. Chapter 4 presents the uses of empirical Bayes method for adjusting the incidence rate of diseases and disease mapping. Chapter 5 describes and demonstrates the methods for visualizing and analyzing the diseases patterns of the study area during 2001-2009 and result. Chapter 6 describes and demonstrates the spatial statistical approach for detecting the disease hotspots using fuzzy logic under different years related climate variability and illustrating the result with the hotspot maps. Chapter 7 describes the use of information value computational and analysis for investigating and visualizing the spatial dynamics of disease outbreaks. The last chapter, Chapter 8 ends the main part of dissertation by outlining the conclusions and recommendations for further study.
Results and Discussion
2. Literature review
3. Study area and data used
4. Disease mapping
6. Risk zone map
7. Fuzzy analysis
8. Conclusions & recommendations
5. Spatial patterns
The structure of dissertation is summarized in the flowchart shown in Figure 1.1:
Figure 1.1 Structure of the dissertation
1.4 The scope of this study
Chiang Mai province was chosen as study area, covering 22,061.17 sq.km.
The selected diseases in this study cover eight endemic diseases: diarrhea, food poisoning, Dysentery unspecified, Amoebic Dysentery, dengue fever (DF), dengue hemorrhagic fever (DHF), pneumonia and fever of unknown origin (FUO).
GIS data were obtained from Geo-Informatics and Space Technology Centre (Northern Region) (GISTC).
Epidemical and population data were obtained from the Chiang Mai Provincial Public Health Office (CMPHO).
Meteorological data were received from Northern Meteorological Center (NMC).