Study of the variation of schistosomiasis risk in Lake Poyang in the People's Republic of China using multiple space-borne sensors for monitoring and modelling

Submitted: 10 December 2014
Accepted: 10 December 2014
Published: 1 May 2014
Abstract Views: 1401
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The dynamics of the Poyang Lake in Jiangxi province, People's Republic of China has been monitored to demonstrate the association of various variables with the distribution of schistosomiasis transmission with particular reference to the annual variation of the habitats for the Oncomelania snail, the intermediate host of Schistosoma japonicum. This was studied with multiple space-borne sensors, including the ENVISAT radar altimeter (RA-2) and MODIS/Terra radiometry data products such as the 16-day enhanced vegetation index, the 8-day sun reflectance, and the derived modified normalized difference water index. The measurements of physical properties were in good accordance with previous reports based on in situ gauge data, spectroradiometry and other optical methods, which encouraged us to build a predictive model based on reported geospatial constraints to assess the limits of potential variation of the snail habitat areas. The simulated results correspond fairly well with surveys conducted by local authorities showing a correlation coefficient of 0.82 between highpotential habitat areas and local estimates in a 9-year (2002-2010) analysis. Taken together, these data indicate that spaceborne observations and in situ measurements can be integrated and used as a first step of a monitoring system for control and analysis of the potential of schistosomiasis dissemination. Since the true range and intensity of transmission in the study region remain elusive at present, a long-term survey around the lake is warranted to build a robust, parametric model.

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Tseng, K.-H., Liang, S., Ibaraki, M., Lee, H., & Shum, C. K. (2014). Study of the variation of schistosomiasis risk in Lake Poyang in the People’s Republic of China using multiple space-borne sensors for monitoring and modelling. Geospatial Health, 8(2), 353–364. https://doi.org/10.4081/gh.2014.25