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The Association between the Mapping Distribution of Melioidosis Incidences and Meteorological Factors in an Endemic Area: Ubon Ratchathani, Thailand (2009–2018)
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Abstract Melioidosis is a public health problem in the tropical regions, occurring to meteorological variability. For 10 years of melioidosis outbreaks, we create probability maps of melioidosis distribution during 2009–2018 and determine the association with meteorological factors. The monthly average rainfall and incidence of melioidosis were high from July to September but they not significantly associated (P = 0.576). However, the monthly maximum and minimum temperature were significantly associated with melioidosis incidence (P = 0.002 and P = 0.029, respectively). We estimated the spatial distribution of rainfall and maximum and minimum temperature using the Co-Kriging interpolation method which found that the spatial distribution of the melioidosis incidence was significantly associated with rainfall in 2009, 2010, and 2015; with the maximum temperature in 2009, 2010, 2011, 2013, and 2015; and with the minimum temperature in 2010, 2011, and 2015. Our finding approach may support information and classify a pattern for melioidosis distribution. Keywords: Incidence, Melioidosis, Meteorological factors
Title: The Association between the Mapping Distribution of Melioidosis Incidences and Meteorological Factors in an Endemic Area: Ubon Ratchathani, Thailand (2009–2018)
Description:
Abstract Melioidosis is a public health problem in the tropical regions, occurring to meteorological variability.
For 10 years of melioidosis outbreaks, we create probability maps of melioidosis distribution during 2009–2018 and determine the association with meteorological factors.
The monthly average rainfall and incidence of melioidosis were high from July to September but they not significantly associated (P = 0.
576).
However, the monthly maximum and minimum temperature were significantly associated with melioidosis incidence (P = 0.
002 and P = 0.
029, respectively).
We estimated the spatial distribution of rainfall and maximum and minimum temperature using the Co-Kriging interpolation method which found that the spatial distribution of the melioidosis incidence was significantly associated with rainfall in 2009, 2010, and 2015; with the maximum temperature in 2009, 2010, 2011, 2013, and 2015; and with the minimum temperature in 2010, 2011, and 2015.
Our finding approach may support information and classify a pattern for melioidosis distribution.
Keywords: Incidence, Melioidosis, Meteorological factors.
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