Space Technology for Prediction of Scrub Typhus Incidence in Mae Fa Luang District, Chiang Rai Province
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 OLTReferences
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Bonell, A., Lubell, Y., Newton, P. N., Crump, J. A., & Paris, D. H. (2017). Estimating the burden of scrub typhus: A systematic review. PLoS neglected tropical diseases, 11(9), e0005838.
Chaisiri, K.; Cosson, J.-F., Morand, S. (2017). Infection of Rodents by Orientia tsutsugamushi, the Agent of Scrub Typhus in Relation to Land Use in Thailand. Tropical Medicine Infectious Disease, 2(4), 53.
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Gibin, M., Longley, P., Atkinson, P. (2007). Kernel density estimation and percent volume contours in general practice catchment area analysis in urban areas. In The Proceedings of GISRUK.: 11-3.
Jin, H. S., Chu, C., & Han, D. Y. (2013). Spatial distribution analysis of scrub typhus in Korea. Osong public health and research perspectives, 4(1), 4-15.
Kuo, C. C., Huang, J. L., Ko, C. Y., Lee, P. F., & Wang, H. C. (2011). Spatial analysis of scrub typhus infection and its association with environmental and socioeconomic factors in Taiwan. Acta tropica, 120(1-2), 52-58.
Kuo, C. C., Wardrop, N., Chang, C. T., Wang, H. C., & Atkinson, P. M. (2017). Significance of major international seaports in the distribution of murine typhus in Taiwan. PLoS neglected tropical diseases, 11(3), e0005430.
Kwak, J., Kim, S., Kim, G., Singh, V. P., Hong, S., & Kim, H. S. (2015). Scrub typhus incidence modeling with meteorological factors in South Korea. International journal of environmental research and public health, 12(7), 7254-7273.
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Richards, J. (1999). Remote Sensing Digital Image Analysis. Berlin: Springer-Verlag.
Ronysriyom Y, Prasatwit A, Rongsriyam K. (2003). Guide for classification of mites in Thailand. Bangkok: The Agricultural Cooperative Federation of Thailand. (in Thai )
Sirisukkarn N. (2008). Prevention and control of scrub typhus. Bangkok: War Veterans Organization. (in Thai)
Tipayamonkholgul M. (2012). Spatial Epidemiology in public health. Thai Journal of Public Health, 42(3), 44-54.
(in Thai)
Wardrop, N. A., Kuo, C. C., Wang, H. C., Clements, A. C., Lee, P. F., & Atkinson, P. M. (2013). Bayesian spatial modelling and the significance of agricultural land use to scrub typhus infection in Taiwan. Geospatial health, 8(1), 229-239.
Wei, Y., Huang, Y., Luo, L., Xiao, X., Liu, L., & Yang, Z. (2014). Rapid increase of scrub typhus: an epidemiology and spatial-temporal cluster analysis in Guangzhou City, Southern China, 2006–2012. PLoS One, 9(7), e101976
Zanter, K. (2015). Landsat 8 (L8) data user’s handbook. US: Department of the Interior US Geological.
2015/11/แผนพัฒนาจังหวัดเชียงราย57_60_ฉบับทบทวน.pdf (in Thai)
Bonell, A., Lubell, Y., Newton, P. N., Crump, J. A., & Paris, D. H. (2017). Estimating the burden of scrub typhus: A systematic review. PLoS neglected tropical diseases, 11(9), e0005838.
Chaisiri, K.; Cosson, J.-F., Morand, S. (2017). Infection of Rodents by Orientia tsutsugamushi, the Agent of Scrub Typhus in Relation to Land Use in Thailand. Tropical Medicine Infectious Disease, 2(4), 53.
Cohen, J., Cohen, P., West, S. G., and Aiken, L. S. (2013). Applied multiple regression/ correlation analysis for the behavioral sciences. New York: Routledge.
Desktop ESRI ArcGIS. (2014). NDVI function. Retrieved 2018 Jan 30, from http://desktop.arcgis.com/en/arcmap/
10.3/manage-data/raster-and-images/ndvi-function.html
Ferguson, G.A. (1981). Statistical analysis in psychology and education. 5th ed. New York: McGraw-Hill.
Gibin, M., Longley, P., Atkinson, P. (2007). Kernel density estimation and percent volume contours in general practice catchment area analysis in urban areas. In The Proceedings of GISRUK.: 11-3.
Jin, H. S., Chu, C., & Han, D. Y. (2013). Spatial distribution analysis of scrub typhus in Korea. Osong public health and research perspectives, 4(1), 4-15.
Kuo, C. C., Huang, J. L., Ko, C. Y., Lee, P. F., & Wang, H. C. (2011). Spatial analysis of scrub typhus infection and its association with environmental and socioeconomic factors in Taiwan. Acta tropica, 120(1-2), 52-58.
Kuo, C. C., Wardrop, N., Chang, C. T., Wang, H. C., & Atkinson, P. M. (2017). Significance of major international seaports in the distribution of murine typhus in Taiwan. PLoS neglected tropical diseases, 11(3), e0005430.
Kwak, J., Kim, S., Kim, G., Singh, V. P., Hong, S., & Kim, H. S. (2015). Scrub typhus incidence modeling with meteorological factors in South Korea. International journal of environmental research and public health, 12(7), 7254-7273.
Lerdthusnee K. (2017). Scrub Typhus. In conference on vector borne disease research moving towards Thailand 4.0: July. Nonthaburi, Thailand. (in Thai)
Lohsoonthron P. (2010). Epidemiology. Bangkok: Chulalongkorn University. (in Thai)
McFeeters, S. K. (2013). Using the normalized difference water index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sensing, 5(7), 3544-3561.
Mitchell A. (2005). The ESRI Guide to GIS Analysis. 2nd. Redlands: Calif.
Office of Disease prevention & control 1. GIS for national disease surveillance (report 506). Retrieved 2018 Jan 24, from http://223.27.246.155:8010/dpc10/index.php.
Phetsouvanh, R., Sonthayanon, P., Pukrittayakamee, S., Paris, D. H., Newton, P. N., Feil, E. J., & Day, N. P. (2015). The diversity and geographical structure of Orientia tsutsugamushi strains from scrub typhus patients in Laos. PLoS neglected tropical diseases, 9(8), e0004024
Richards, J. (1999). Remote Sensing Digital Image Analysis. Berlin: Springer-Verlag.
Ronysriyom Y, Prasatwit A, Rongsriyam K. (2003). Guide for classification of mites in Thailand. Bangkok: The Agricultural Cooperative Federation of Thailand. (in Thai )
Sirisukkarn N. (2008). Prevention and control of scrub typhus. Bangkok: War Veterans Organization. (in Thai)
Tipayamonkholgul M. (2012). Spatial Epidemiology in public health. Thai Journal of Public Health, 42(3), 44-54.
(in Thai)
Wardrop, N. A., Kuo, C. C., Wang, H. C., Clements, A. C., Lee, P. F., & Atkinson, P. M. (2013). Bayesian spatial modelling and the significance of agricultural land use to scrub typhus infection in Taiwan. Geospatial health, 8(1), 229-239.
Wei, Y., Huang, Y., Luo, L., Xiao, X., Liu, L., & Yang, Z. (2014). Rapid increase of scrub typhus: an epidemiology and spatial-temporal cluster analysis in Guangzhou City, Southern China, 2006–2012. PLoS One, 9(7), e101976
Zanter, K. (2015). Landsat 8 (L8) data user’s handbook. US: Department of the Interior US Geological.
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Published
2022-09-07
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