Spatiotemporal distribution and geostatistically interpolated mapping of the melioidosis risk in an endemic zone in Thailand (original) (raw)
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Chiang Mai University Journal of Natural Sciences, 2021
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Int. J. Environ. Res. Public Health, 2024
Melioidosis is an endemic infectious disease caused by Burkholderia pseudomallei bacteria, which contaminates soil and water. To better understand the environmental changes that have contributed to melioidosis outbreaks, this study used spatiotemporal analyses to clarify the distribution pattern of melioidosis and the relationship between melioidosis morbidity rate and local environmental indicators (land surface temperature, normalised difference vegetation index, normalised difference water index) and rainfall. A retrospective study was conducted from January 2013 to December 2022, covering data from 219 sub-districts in Northeast Thailand, with each exhibiting a varying morbidity rate of melioidosis on a monthly basis. Spatial autocorrelation was determined using local Moran’s I, and the relationship between the melioidosis morbidity rate and the environmental indicators was evaluated using a geographically weighted Poisson regression. The results revealed clustered spatiotemporal patterns of melioidosis morbidity rate across sub-districts, with hotspots predominantly observed in the northern region. Furthermore, we observed a range of coefficients for the environmental indicators, varying from negative to positive, which provided insights into their relative contributions to melioidosis in each local area and month. These findings highlight the presence of spatial heterogeneity driven by environmental indicators and underscore the importance of public health offices implementing targeted monitoring and surveillance strategies for melioidosis in different locations.
International Journal of Environmental Research and Public Health, 2013
Hand, Foot and Mouth Disease (HFMD) is an emerging viral disease, and at present, there are no antiviral drugs or vaccines available to control it. Outbreaks have persisted for the past 10 years, particularly in northern Thailand. This study aimed to elucidate the phenomenon of HFMD outbreaks from 2003 to 2012 using general statistics and spatial-temporal analysis employing a GIS-based method. The spatial analysis examined data at the village level to create a map representing the distribution pattern, mean center, standard deviation ellipse and hotspots for each outbreak. A temporal analysis was used to analyze the correlation between monthly case data and meteorological factors. The results indicate that the disease can occur at any time of the year, but appears to peak in the rainy and cold seasons. The distribution of outbreaks exhibited a clustered pattern. Most mean centers and standard deviation ellipses occurred in similar areas. The linear directional mean values of the outbreaks were oriented toward the south. When separated by season, it was found that there was a significant correlation with the direction of the southwest monsoon at the same time. An autocorrelation analysis revealed that hotspots tended to increase even when patient cases subsided. In particular, a new hotspot was found in the recent year in Mae Hong Son province.
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Dengue is a microorganism sickness transmitted by the yellow-fever (mosquito Aedes aegypti) mosquito. The global incidence of dengue has grown dramatically in recent decades. In Madurai, dengue fever and dengue hemorrhagic fever has shown an increasing trend. Data associated with dengue fever was gathered from the varied government health agencies. This study analysed dengue cases from 2009-2015 in different precincts in Madurai city. It associates with ''Z'' score variation based on GIS techniques. Moran's I, average nearest neighborhood and kernel density estimation were used to access spatial distribution cases. The result showed that dengue cases were spatially random (p \ 0.0001) by using spatial autocorrelation analysis showed dengue cases within the Madurai wards were highly clustered and occurred at an average distance of 143.56 m. Several locations, especially residential areas had been identified as hot spots of dengue cases in the Madurai city used by using kernel density estimation analysis. It will helpful for health officers in developing efficient control measures and contingency programs in identifying and prioritizing their efforts ineffective dengue control activities.
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Dengue fever (DF) and dengue haemorrhagic fever (DHF) pose a constant serious risk and continue to be a major public health threat in Thailand. A better understanding of the factors responsible for this affliction will enable a more precise prediction of the location and time of high-risk events. Mapping spatial distribution of disease occurrence and risk can serve as a useful tool for identifying exposures of public health concern. A Geographical Information System (GIS)-based methodology to investigate the relationship between the reported incidence of dengue fever and spatial patterns in nine districts of northern Thailand was analysed for the years 1999 to 2003. From the average prevalence of dengue cases in each district in different years, it is apparent that 2001 had the highest values, followed by 2002, 1999, 2000 and 2003 in that order. With Moran’s I and Geary’s Ratio, only the year 2001 showed spatial patterns with statistical significance.
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International Journal of Health Geographics, 2009
Background: Diarrhea is a major public health problem in Thailand. The Ministry of Public Health, Thailand, has been trying to monitor and control this disease for many years. The methodology and the results from this study could be useful for public health officers to develop a system to monitor and prevent diarrhea outbreaks.