4.5 Article

Spatio-temporal Transmission and Environmental Determinants of Schistosomiasis Japonica in Anhui Province, China

Journal

PLOS NEGLECTED TROPICAL DISEASES
Volume 9, Issue 2, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pntd.0003470

Keywords

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Funding

  1. National Natural Science Foundation of China [81102167, 81172609, J1210041]
  2. Specialized Research Fund for the Doctoral Program of Higher Education, SRFDP [20110071120040]
  3. Foundation for the Author of National Excellent Doctoral Dissertation of PR China (FANEDD) [201186]
  4. National ST Major Program [2012ZX10004220, 2008ZX10004-011]
  5. Talent Programs for Fostering Outstanding Youth of Shanghai [XYQ2013071]
  6. Independent Research Project of Fudan University [20520133105]
  7. Opening Fund of Key Laboratory of Poyang Lake Wetland and Watershed Research(Jiangxi Normal University), Ministry of Education [PK2014001]
  8. China Postdoctoral Science Foundation [2014M560291]

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Background Schistosomiasis japonica still remains of public health and economic significance in China, especially in the lake and marshland areas along the Yangtze River Basin, where the control of transmission has proven difficult. In the study, we investigated spatio-temporal variations of S. japonicum infection risk in Anhui Province and assessed the associations of the disease with key environmental factors with the aim of understanding the mechanism of the disease and seeking clues to effective and sustainable schistosomiasis control. Methodology/Principal Findings Infection data of schistosomiasis from annual conventional surveys were obtained at the village level in Anhui Province, China, from 2000 to 2010 and used in combination with environmental data. The spatio-temporal kriging model was used to assess how these environmental factors affected the spatio-temporal pattern of schistosomiasis risk. Our results suggested that seasonal variation of the normalized difference vegetation index (NDVI), seasonal variation of land surface temperature at daytime (LSTD), and distance to the Yangtze River were negatively significantly associated with risk of schistosomiasis. Predictive maps showed that schistosomiasis prevalence remained at a low level and schistosomiasis risk mainly evolved along the Yangtze River. Schistosomiasis risk also followed a focal spatial pattern, fluctuating temporally with a peak (the largest spatial extent) in 2005 and then contracting gradually but with a scattered distribution until 2010. Conclusion The fitted spatio-temporal kriging model can capture variations of schistosomiasis risk over space and time. Combined with techniques of geographic information system (GIS) and remote sensing (RS), this approach facilitates and enriches risk modeling of schistosomiasis, which in turn helps to identify prior areas for effective and sustainable control of schistosomiasis in Anhui Province and perhaps elsewhere in China.

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