Space Technology for Prediction of Scrub Typhus Incidence in Mae Fa Luang District, Chiang Rai Province

Authors

  • Pimpakran Boonsawat คณะสังคมศาสตร์ มหาวิทยาลัยเชียงใหม่
  • Phonpat Hemwan คณะสังคมศาสตร์ มหาวิทยาลัยเชียงใหม่
  • Arisara Charoenpanyanet คณะสังคมศาสตร์ มหาวิทยาลัยเชียงใหม่

Abstract

The purposes of this study were: 1) to analyze distribution pattern and density of scrub typhus in Mae Fah Luang District, Chiang Rai Province. 2) to create of regression model for predicting the incidence of scrub typhus during December 2018. This paper discusses point pattern analysis of cases using the Average Nearest Neighbor Index and Kernel Density Estimation. The modeling section was performed using Landsat-8 OLT satellite imagery. The physical factors were determined, including NDVI, NDWI, temperature, height and land use. These factors were then analyzed by Pearson's correlation coefficient. The data of physical factors was used to the independent variable to create the Prediction of Scrub Typhus Incidence model. The results showed Scrub Typhus Incidence are scattered in many areas of Mae Fah Luang District. Most of the outbreak areas were in the northern of Mae Fah Luang District and decreased as they approached Mueang Chiang Rai District. Scrub Typhus are most in Rural area because community in Mae Fah Luang District, adjacent to the edge of the forest resulting in an outbreak near the village. In addition, distribution is random pattern in January-May and clustered pattern in June-December. The incidences were distributed and the severity of the disease increased in June-December, the highest in rainy, winter. and summer respectively. The critical areas from the density analysis were at Ban Thoet Thai, Ban Hin Taek, Ban Huai Eun and Ban Thoet Thai Nueng, respectively. In particular, The regression model had relationship with NDVI (0.5 - 0.6), NDWI (-0.7)-(-0.5), temperature 20 – 26 °C, hight of 400 – 1000 meters MSL and R2 of 0.761. Keywords :  Scrub Typhus ; Geo-Information techniques ; Spatial Analysis ; Spatial statistics ; Landsat-8 OLT

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Published

2022-09-07