Exploring the Relationship between Melioidosis Morbidity Rate and Local Environmental Indicators Using Remotely Sensed Data.

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.

Spatiotemporal distribution and geostatistically interpolated mapping of the melioidosis risk in an endemic zone in Thailand.

Melioidosis, a bacterial, infectious disease contracted from contaminated soil or water, is a public health problem identified in tropical regions and endemic several regions of Thailand. Surveillance and prevention are important for determining its distribution patterns and mapping its risk, which have been analysed in the present study. Case reports in Thailand were collected from 1 January 2016 to 31 December 2020. Spatial autocorrelation was analyzed using Moran's I and univariate local Moran's I. Spatial point data of melioidosis incidence were calculated, with riskmapping interpolation performed by Kriging. It was highest in 2016, at 32.37 cases per 100,000 people, and lowest in 2020, at 10.83 cases per 100,000 people. General observations revealed that its incidence decreased slightly from 2016 to 2018 and drastically in 2019 and 2020. The Moran's I values for melioidosis incidence exhibited a random spatial pattern in 2016 and clustered distribution from 2017 to 2020. The risk and variance maps show interval values. These findings may contribute to the monitoring and surveillance of melioidosis outbreaks.

Mapping the probability of detecting Burkholderia pseudomallei in rural rice paddy soil based on indicator kriging and spatial soil factor analysis.

BACKGROUND: Melioidosis is an infectious disease commonly found in Thailand. This infectious disease is caused by Burkholderia pseudomallei in soil. This study aims to analyze the association between spatial soil factors and B. pseudomallei detection, as well as to map the probability of B. pseudomallei contamination based on indicator kriging in paddy soil. METHODS: Seventy-eight soil samples were collected randomly on 22 April 2018 in various paddy fields. Oxidase, Gram staining and monoclonal antibody-based latex agglutination assays were performed to confirm the presence of B. pseudomallei in soil samples. The association between B. pseudomallei detection and spatial soil factors including soil temperature, soil pH, soil texture and soil drainage were analyzed by the Mann-Whitney U test and χ2 test. Subsequently, a semivariogram model and indicator kriging were used to map the probability of B. pseudomallei contamination. RESULTS: Of the 78 samples, B. pseudomallei was detected in 32 (41.03%). The presence or absence of B. pseudomallei was not significantly associated with spatial soil factors. The semivariogram model showed that the lag distance between positive B. pseudomallei samples was 90.51 m. CONCLUSION: The empirical semivariogram and indicator kriging are an alternative option for predicting the spatial distribution of B. pseudomallei in soil.

Perceptions and risky behaviors associated with Leptospirosis in an endemic area in a village of Ubon Ratchathani Province, Thailand.

BACKGROUND: Leptospirosis, a disease caused by Leptospira species, a spirochaete bacterium that can develop in an appropriate environment and/or grow in human and/or animal hosts, is a serious problem for the Ministry of Public Health, Thailand. OBJECTIVE: To investigate people's perceptions and behavioral risks regarding leptospirosis infection. METHODS: The cross-sectional descriptive study collected data in May, 2013. Data on individuals' perceptions and risky behaviors concerning leptospirosis were collected from 104 completed questionnaires. RESULTS: Regarding perceptions of leptospirosis, we found them to be at a high level (97.1%) and risky behaviors regarding leptospirosis were reported at a moderate level (74.0%). The study found no correlation between perceptions and risky behaviors regarding leptospirosis (r 0.186, p-value 0.059). CONCLUSION: This study suggest that people in these areas have good knowledge about leptospirosis. However, some people have risky behavior associated with leptospirosis. Thus, a behavioral change campaign should be promoted to encourage people awareness of the dangers of such behavior.

Geo-database use to promote dengue infection prevention and control.

Dengue infection (DI) is a major health problem in Thailand and is especially prevalent in Ubon Ratchathani Province. The objectives of the project were: (1) to develop a geo-database system for DI prevention and control, (2) to perform an Aedes aegypti larval vector survey for DI prevention and control in Ubon Ratchathani Province, (3) to study the behavior and perceptions regarding DI prevention among the target population in Ubon Ratchathani Province. Ten villages with high incidences of DI over a 3 year period from 2005 to 2007 were selected. The survey was divided into 2 periods, pre-outbreak period (February-April 2008) and outbreak period (June-August 2008). The data were collected in April and June 2008. The households in each village were purposively sampled. Water containers inside and outside of the houses were surveyed using the World Health Organization's house index (HI), container index (CI), and Breteau index (BI). The location of each household was recorded using the global positioning system (GPS). Data regarding people's perceptions and behaviors concerning DI prevention were collected during interviews of 383 families in Mach 2008. A database for DI was developed using ArcView version 9.2. The results showed during the pre-outbreak period, Non Jig, Non Sawang, and Huai Teeneu villages had the highest risk level (BI > or =50). During the outbreak period, Non Jig and Huai Teeneu village had the highest risk level (BI > or =50). Results regarding DI perceptions showed the target population had high levels of DI perceptions. DI preventive behavior was found in 50.9%.