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

Abstract

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusion</jats:title> <jats:p>The empirical semivariogram and indicator kriging are an alternative option for predicting the spatial distribution of B. pseudomallei in soil.</jats:p> </jats:sec>

Spatial Distribution and Epidemiology of Echinococcus Granulosus Infection in Sheep and Goats Slaughtered in a Hyperendemic European Mediterranean Area

“…The second step involved estimation mapping to predict the presence or absence of disease in an unknown location. Indicator kriging was used to estimate mapping distributions under a given threshold (zk) [18]. The resulting data were interpreted as values between zero and one.…”

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

“…Understanding its spatial clustering makes surveillance and control easier than if the infection were randomly distributed and dispersed. Armed with knowledge of which sub-districts exhibit highly clustered melioidosis distributions, the Tambon Health Promoting Hospital (the and 90.51 m (9.24 km 2 ), respectively Saengnill et al, 2020). Here, however, we focused on a region where there is a high risk of B. pseudomallei contamination in both soil and water, which should improve the identification of high-risk zones and account for uncertainty regarding their distribution and size as well as reveal incidence clusters.…”

“…Melioidosis has been identified in tropical regions and particularly in Thailand, where it is endemic, with reported incidences of 4.07, 4.24, and 4.21 cases per 100,000 people in , 2019, respectively (Bureau of Epidemiology, 2023. The bacterium has been identified in paddy soil, disused land and water supplies (Finkelstein et al, 2000;Limmathurotsakul et al, 2013;Saengnill et al, 2020;Wang-Ngarm et al, 2014). Several studies on its clinical epidemiology and increasing incidence in humans in north-eastern Thailand and neighbouring countries, e.g., Laos have provided an important basis of knowledge for future research (Chen et al, 2012;Dance et al, 2018;Hantrakun et al, 2019;.…”

“…This study aimed to analyse the spatial autocorrelation and spatial pattern of melioidosis risk, and to create incidence risk maps by applying geostatistics, a quantitative field within GIS, an approach that can analyse variance in distance from sampling points and also calculate spatial correlations for single variables, where closer points are considered more similar. Geostatistical tools have been used to map the probability of disease occurrence by determining the lag distances between sites (paddy fields) positive for B. pseudomallei Saengnill et al, 2020). We used a semivariograms to determine the spatial autocorrelation of the lag distances, and indicator Kriging interpolation to generate the risk map of melioidosis incidence.…”

Environmental Factors Associated With Soil Prevalence of the Melioidosis Pathogen Burkholderia pseudomallei: A Longitudinal Seasonal Study From South West India

“…A limitation of this resource-saving single target TTSS1 qPCR approach of our main study, is the potential underestimation of some samples with very low DNA amounts, since those samples might not be reliably detected by a single PCR assay (Gohler et al, 2017). Soil sampling at 30 cm depth was chosen, since this sampling depth has been applied in numerous studies (Wuthiekanun et al, 2005;Kaestli et al, 2007Kaestli et al, , 2009Limmathurotsakul et al, 2010;Trung et al, 2011;Hantrakun et al, 2016;Manivanh et al, 2017;Dance et al, 2018;Saengnill et al, 2020;Swe et al, 2021) and the bacterial load at this depth is less likely to be influenced by short term environmental influences such as, e.g., UV light compared to soil surface. Still a depth in the range of 30 cm is accessible during agriculture activities such as plowing and cultivating soil leading to potential human exposition.…”