Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015

Submitted: 8 January 2020
Accepted: 22 August 2020
Published: 26 November 2020
Abstract Views: 2589
PDF: 786
HTML: 44
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

This study statistically identified the association of remotely sensed environmental factors, such as Land Surface Temperature (LST), Night Time Light (NTL), rainfall, the Normalised Difference Vegetation Index (NDVI) and elevation with the incidence of leptospirosis in Thailand based on the nationwide 7,495 confirmed cases reported during 2013-2015. This work also established prediction models based on empirical findings. Panel regression models with random-effect and fixed-effect specifications were used to investigate the association between the remotely sensed environmental factors and the leptospirosis incidence. The Local Indicators of Spatial Association (LISA) statistics were also applied to detect the spatial patterns of leptospirosis and similar results were found (the R2 values of the random-effect and fixed-effect models were 0.3686 and 0.3684, respectively). The outcome thus indicates that remotely sensed environmental factors possess statistically significant contribution in predicting this disease. The highest association in 3 years was observed in LST (random- effect coefficient = -9.787, P<0.001; fixed-effect coefficient = -10.340, P=0.005) followed by rainfall (random-effect coefficient = 1.353, P<0.001; fixed-effect coefficient = 1.347, P<0.001) and NTL density (random-effect coefficient = -0.569, P=0.004; fixed-effect coefficient = -0.564, P=0.001). All results obtained from the bivariate LISA statistics indicated the localised associations between remotely sensed environmental factors and the incidence of leptospirosis. Particularly, LISA's results showed that the border provinces in the northeast, the northern and the southern regions displayed clusters of high leptospirosis incidence. All obtained outcomes thus show that remotely sensed environmental factors can be applied to panel regression models for incidence prediction, and these indicators can also identify the spatial concentration of leptospirosis in Thailand.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Adeola A, Botai J, Mukarugwiza Olwoch J, de W. Rautenbach H, Adisa O, de Jager C, Botai C, Aaron M, 2019. Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa. GH 14:81-91. DOI: https://doi.org/10.4081/gh.2019.676
Anselin L, 1995. Local Indicators of Spatial Association (LISA). Geogr Anal 27:93-115. DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Anselin L, 2003. An introduction to spatial autocorrelation analysis with GeoDa. Spatial Analysis Laboratory, University of Illinois. Champagne-Urbana, Illinois.
Anselin L, 2004. Review of Cluster Analysis Software. The North American Association of Central Cancer Registries, Inc.
Anselin L, Syabri I, Kho Y, 2006. GeoDa: an introduction to spatial data analysis. Geogr Anal 38:5-22. DOI: https://doi.org/10.1111/j.0016-7363.2005.00671.x
Basnyat B., Cumbo TA, Edelman R, 2001. Infections at high altitude. Clinical infectious diseases, 1887-1891. DOI: https://doi.org/10.1086/324163
Chadsuthi S, Modchang C, Lenbury Y, Iamsirithaworn S, Triampo W, 2012. Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time–series and ARIMAX analyses. Asian Pac. J. Trop. Med 5:539-46. DOI: https://doi.org/10.1016/S1995-7645(12)60095-9
Dhewantara PW, Hu W, Zhang W, Yin W, Ding F, Mamun A, Soare RM, 2019. Leptospirosis, Climate and Satellite-based Environmental Factors: A Temporal Modeling. OJPHI 11:e396. DOI: https://doi.org/10.5210/ojphi.v11i1.9879
Dhewantara PW, Zhang W, Al Mamun A, Yin WW, Ding F, Guo D, Hu W, Soares Magalhães, RJ, 2020. Spatial distribution of leptospirosis incidence in the Upper Yangtze and Pearl River Basin, China: Tools to support intervention and elimination. The Science of the total environment, 725:138251. DOI: https://doi.org/10.1016/j.scitotenv.2020.138251
Diaz, JH, 2015. Rodent-borne infectious disease outbreaks after flooding disasters: Epidemiology, management, and prevention. Am. J. Disaster Med 10:259-67. DOI: https://doi.org/10.5055/ajdm.2015.0207
Gao B, Huang Q, He C, Ma Q, 2015. Dynamics of urbanization levels in China from 1992 to 2012: Perspective from DMSP/OLS nighttime light data. Remote Sens 7:1721-35. DOI: https://doi.org/10.3390/rs70201721
George J, Po-Huang C, Tyler S, Rachel B, William K, Bereketab L, Hui-Chen T, Christina W, 2013. Use of GIS mapping as a public health tool: from cholera to cancer. Health Serv Insights 6:111-6. DOI: https://doi.org/10.4137/HSI.S10471
Gracie R, Barcellos C, Magalhães M, Souza-Santos R, Barrocas PRG, 2014. Geographical scale effects on the analysis of leptospirosis determinants. Int. J. Environ. Res. Public Health 11:10366-83. DOI: https://doi.org/10.3390/ijerph111010366
Haake DA, Levett PN, 2015. Leptospirosis in humans. Curr Top Microbiol Immunol 387:65-97. DOI: https://doi.org/10.1007/978-3-662-45059-8_5
Henderson JV, Storeygard A, Weil DN, 2012. Measuring economic growth from outer space. Am Econ Rev 102:994–1028. DOI: https://doi.org/10.1257/aer.102.2.994
Herbreteau V, Demoraes F, Khaungaew W, Hugot JP, Gonzalez JP, Kittayapong P, Souris M, 2006. Use of geographic information system and remote sensing for assessing environment influence on leptospirosis incidence, Phrae province, Thailand. International Journal of Geoinformatics, 2: 43-50.
Herbreteau V, Salem G, Souris M, Hugot JP, Gonzalez JP, 2007. Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration. Health Place 13:400-03. DOI: https://doi.org/10.1016/j.healthplace.2006.03.003
Hinjoy S, 2014. Epidemiology of leptospirosis from Thai national disease surveillance system, 2003-2012, OSIR, 7(2):1-5.
Hsiao, C, 2007. Panel data analysis—advantages and challenges. TEST 16:1–22. DOI: https://doi.org/10.1007/s11749-007-0046-x
Gonwong S, Chuenchitra T, Khantapura P, Islam D, Ruamsap N, Swierczewski BE, Mason CJ, 2017. Nationwide seroprevalence of leptospirosis among young Thai men, 2007-2008. The American journal of tropical medicine and hygiene, 97(6): 1682–1685. DOI: https://doi.org/10.4269/ajtmh.17-0163
Laohasiriwong W, Puttanapong N, Luenam A, 2018. A comparison of spatial heterogeneity with local cluster detection methods for chronic respiratory diseases in Thailand. F1000Res 6:1819. DOI: https://doi.org/10.12688/f1000research.12128.2
Lau CL, Clements ACA, Skelly C, Dobson AJ, Smythe LD, Weinstein P, 2012. Leptospirosis in American Samoa – estimating and mapping risk using environmental data. PLoS Negl Trop Dis 6:e1669. DOI: https://doi.org/10.1371/journal.pntd.0001669
Ledien J, Sorn S, Hem S, Huy R, Buchy P, Tarantola A, Cappelle J, 2017. Assessing the performance of remotely-sensed flooding indicators and their potential contribution to early warning for leptospirosis in Cambodia. PloS one, 12: e0181044. DOI: https://doi.org/10.1371/journal.pone.0181044
Li D, Zhao X, Li X, 2016a. Remote sensing of human beings–a perspective from nighttime light. Geo Spat Inf Sci 19:69–79. DOI: https://doi.org/10.1080/10095020.2016.1159389
Li Q, Lu L, Weng Q, Xie Y, Guo H, 2016b. Monitoring urban dynamics in the southeast USA using time-series DMSP/OLS nightlight imagery. Remote Sens 8:578. DOI: https://doi.org/10.3390/rs8070578
Luenam A, Puttanapong N, 2019. Spatial and statistical analysis of leptospirosis in Thailand from 2013 to 2015. GH 14:121-27. DOI: https://doi.org/10.4081/gh.2019.739
Ministry of Public Health (MoPH), 2006. National Disease Surveillance 2000-2006. The War Veterans Organization of Thailand.
Moran PAP, 1950. Notes on continuous stochastic phenomena. Biometrika. 37:17–23. DOI: https://doi.org/10.1093/biomet/37.1-2.17
Parker J, Walker M, 2011. Survival of a pathogenic Leptospira serovar in response to combined in vitro pH and temperature stresses. Vet. Microbiol 152: 146–150. DOI: https://doi.org/10.1016/j.vetmic.2011.04.028
Skouloudis AN, Rickerby DG, 2015. In-situ and remote sensing networks for environmental monitoring and global assessment of leptospirosis outbreaks. Procedia Eng 107:194-204. DOI: https://doi.org/10.1016/j.proeng.2015.06.074
Steiniger S, Hunter AJ, 2013. The 2012 free and open source GIS software map: A guide to facilitate research, development, and adoption. Comput Environ Urban Syst 39:136-150. DOI: https://doi.org/10.1016/j.compenvurbsys.2012.10.003
Sunaryo S, Widiastuti D, 2012. Mapping of leptospirosis risk factor based on remote sensing image in Tembalang, Semarang City, Central Java. Health Sci. J. Indones 3:45-50.
Torres-Reyna O, 2007. Panel data analysis fixed and random effects using Stata (v. 4.2). Data & Statistical Services, Priceton University.
Triampo W, Baowan D, Tang I, Nuttavut N, Wong-Ekkabut J, Doungchawee G, 2007. A simple deterministic model for the spread of leptospirosis in Thailand. Int J Bio Med Sci 2:22-6.
TRMM (Tropical Rainfall Measuring Mission), 2011. TRMM (TMPA/3B43) Rainfall estimate L3 1 month 0.25 degree x 0.25 degree V7, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC). Available from: doi: https://doi.org/10.5067/TRMM/TMPA/MONTH/7
Wan Z, 2007. Collection-5 MODIS land surface temperature products usersguide. ICESS, University of California, Santa Barbara.
World Health Organization (WHO), 2003. Human leptospirosis: guidance for diagnosis, surveillance and control. WHO, Geneva, Switzerland. Available from: http://www.who.int/iris/handle/10665/42667.

How to Cite

Luenam, A., & Puttanapong, N. (2020). Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015. Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.856

List of Cited By :

Crossref logo