Year: 2023
Type: article
Abstract: Leptospirosis is a major public health problem and it has been occurring outbreaks. The monitoring and surveillance should to use the geospatial health data for classification of spatial distribution pattern and predictive the risk area. This study aims to 1) analyst the pattern of spatial distribution and hot spot for leptospirosis, and 2) predict the spatial risk of leptospirosis. The data of leptospirosis cases was collected during 2017-2021 and georeferenced with village location. Then, the data was determined a pattern of spatial distribution using Univariate Moran’s I and hot spot using Local Getis-Ord Gi*. After that, the spatial risk was interpolated with Inverse distance weighting (IDW). The result showed that the spatial distribution had cluster pattern and Moran’s I correlation was 0.079. Hot spot of leptospirosis was 49 villages, while the spatial risk of leptospirosis found that the highest was 6.974 and the lowest was less than 0.001 (mean 1.432, S.D. 0.502). Then, the spatial risk was classified into 4 levels which were verry high, high, moderate, and low. Therefore, the application of geographic information system could be analyzed the pattern of spatial distribution and prediction of spatial risk. This information may be implement for monitoring and survillence of leptospirosis.
Language: Thai
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7216948151