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

Jaruwan Wongbutdee, Jutharat Jittimanee, Suwaporn Daendee, Pongthep Thongsang and Wacharapong Saengnill ()
Additional contact information
Jaruwan Wongbutdee: Geospatial Health Research Group, College of Medicine and Public Health, Ubon Ratchathani University, Ubonratchathani 34190, Thailand
Jutharat Jittimanee: Geospatial Health Research Group, College of Medicine and Public Health, Ubon Ratchathani University, Ubonratchathani 34190, Thailand
Suwaporn Daendee: Geospatial Health Research Group, College of Medicine and Public Health, Ubon Ratchathani University, Ubonratchathani 34190, Thailand
Pongthep Thongsang: Department of Geology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
Wacharapong Saengnill: Geospatial Health Research Group, College of Medicine and Public Health, Ubon Ratchathani University, Ubonratchathani 34190, Thailand

IJERPH, 2024, vol. 21, issue 5, 1-18

Abstract: 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.

Keywords: geographically weighted Poisson regression; Google Earth engine; spatial model; Burkholderia pseudomallei (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/21/5/614/pdf (application/pdf)
https://www.mdpi.com/1660-4601/21/5/614/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:21:y:2024:i:5:p:614-:d:1393560

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().