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

Abstract

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.

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

“…Conversely, tambons initially deemed low-high may transition into high-risk areas because of their proximity to high-risk groups. Notably, the high-risk group was primarily clustered in the northern region, consistent with the findings of Wongbutdee et al [21], who identified a significantly elevated incidence of melioidosis in northern Ubon Ratchathani during 2016-2020. Clustering of melioidosis cases suggests heightened exposure to B. pseudomallei in these areas [43].…”

“…Geospatial data, which are data with georeferenced coordinates to positions on the Earth's surface, have been utilised to monitor and investigate the risk area of melioidosis occurrence. Several studies have conducted spatial analyses of melioidosis distribution in the endemic regions of Australia [2,18], Thailand [19][20][21], and Laos [22]. Geostatistical modelling has revealed that the range distance of the spatial autocorrelation in a quantitative B. pseudomallei count was 7.6 m [23], and the range distance between positive B. pseudomallei samples was 90.51 m in a rice field [24].…”

“…For vector-borne diseases such as malaria, dengue fever [25,26], and COVID-19 [27,28], the platform can play a crucial role in understanding disease dynamics and improving public health interventions. In the case of melioidosis, a study revealed the spatial distribution pattern of melioidosis incidence using local Moran's I and its spatial risk area using indicator interpolation kriging [21]. However, this study only considered a single variable.…”