Volume 98, Issue 4
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645



Schistosomiasis is still prevalent in some parts of China. A shift in strategy from morbidity control to elimination has led to great strides in the past several decades. The objective of this study was to explore the spatial and temporal characteristics of schistosomiasis in Anhui, an eastern province of China. In this study, township-based parasitological data were collected from annual cross-sectional surveys during 1997–2010. The kernel -means method was used to identify spatial clusters of schistosomiasis, and an empirical mode decomposition technique was used to analyze the temporal trend for in each clustered region. Overall, the prevalence of schistosomiasis remained at a low level except for the resurgence in 2005. According to the Caliński–Harabas index, all the townships were classified into three different clusters (median prevalence: 3.6 per 10,100, 1.8 per 10,000 and 1.7 per 10,000), respectively representing high-, median-, and low-risk clusters. There was an increasing tendency observed for the disease over time. The prevalence increased rapidly from 2003 to 2005, peaked in 2006, and then decreased afterward in the high-risk cluster. A moderate increase was observed in the median-risk cluster from 1998 to 2006, but there was an obvious decreasing tendency in the low-risk cluster after the year 2000. The spatial and temporal patterns of schistosomiasis were nonsynchronous across the three clusters. Disease interventions may be adjusted according to the risk levels of the clusters.


Article metrics loading...

The graphs shown below represent data from March 2017
Loading full text...

Full text loading...



  1. Colley DG, Bustinduy AL, Secor WE, King CH, , 2014. Human schistosomiasis. Lancet 383: 22532264. [Google Scholar]
  2. Steinmann P, Keiser J, Bos R, Tanner M, Utzinger J, , 2006. Schistosomiasis and water resources development: systematic review, meta-analysis, and estimates of people at risk. Lancet Infect Dis 6: 411425. [Google Scholar]
  3. Ross AG, Sleigh AC, Li Y, Davis GM, Williams GM, Jiang Z, Feng Z, McManus DP, , 2001. Schistosomiasis in the People’s Republic of China: prospects and challenges for the 21st century. Clin Microbiol Rev 14: 270295. [Google Scholar]
  4. Xu J, 2016. Enhancing collaboration between China and African countries for schistosomiasis control. Lancet Infect Dis 16: 376383. [Google Scholar]
  5. WHO, 2016. Schistosomiasis. Available at: http://www.who.int/mediacentre/factsheets/fs115/en/. Accessed January 26, 2018.
  6. Rollinson D, 2013. Time to set the agenda for schistosomiasis elimination. Acta Trop 128: 423440. [Google Scholar]
  7. Hotez PJ, 2014. The global burden of disease study 2010: interpretation and implications for the neglected tropical diseases. PLoS Negl Trop Dis 8: e2865. [Google Scholar]
  8. The Carter Center, 2014. Schistosomiasis (Bilharziasis) Control Program. Available at: https://www.cartercenter.org/health/schistosomiasis/index.html. Accessed January 26, 2018.
  9. Zhou XN, 2007. Epidemiology of schistosomiasis in the People’s Republic of China, 2004. Emerg Infect Dis 13: 14701476. [Google Scholar]
  10. Zhang Z, Zhu R, Ward MP, Xu W, Zhang L, Guo J, Zhao F, Jiang Q, , 2012. Long-term impact of the World Bank Loan Project for schistosomiasis control: a comparison of the spatial distribution of schistosomiasis risk in China. PLoS Negl Trop Dis 6: e1620. [Google Scholar]
  11. Hu Y, 2016. Monitoring schistosomiasis risk in East China over space and time using a Bayesian hierarchical modeling approach. Sci Rep 6: 24173. [Google Scholar]
  12. Hu Y, Xiong C, Zhang Z, Luo C, Ward M, Gao J, Zhang L, Jiang Q, , 2014. Dynamics of spatial clustering of schistosomiasis in the Yangtze River Valley at the end of and following the World Bank Loan Project. Parasitol Int 63: 500505. [Google Scholar]
  13. Liang S, Seto EYW, Remais JV, Zhong B, Yang C, Hubbard A, Davis GM, Gu X, Qiu D, Spear RC, , 2007. Environmental effects on parasitic disease transmission exemplified by schistosomiasis in western China Proc Natl Acad Sci USA 104: 71107115. [Google Scholar]
  14. Zhou XN, Wang LY, Chen MG, Wu XH, Jiang QW, Chen XY, Zheng J, Utzinger J, , 2005. The public health significance and control of schistosomiasis in China—then and now. Acta Trop 96: 97105. [Google Scholar]
  15. WHO, 2013. Regional Action Plan for Neglected Tropical Diseases in the Western Pacific (2012–2016). Manila, Philippines: WHO Regional Office for the Western Pacific.
  16. Xia C, Bergquist R, Lynn H, Hu F, Lin D, Hao Y, Li S, Hu Y, Zhang Z, , 2017. Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China. Parasit Vectors 10: 136. [Google Scholar]
  17. Sun L, 2015. Identifying spatial clusters of schistosomiasis in Anhui Province of China: a study from the perspective of application. Int J Environ Res Public Health 12: 1175611769. [Google Scholar]
  18. Huang NE, Wu MLC, Long SR, Shen SSP, Qu W, Gloersen P, Fan KL, , 2003. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc R Soc: Math Phys Eng Sci 459: 23172345. [Google Scholar]
  19. Huang NE, Shen Z, Long SR, Wu M, Shih HH, Zheng QN, Yen NC, Tung CC, Liu HH, , 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc: Math Phys Eng Sci 454: 903995. [Google Scholar]
  20. Hu M, Jia L, Wang J, Pan Y, , 2013. Spatial and temporal characteristics of particulate matter in Beijing, China using the empirical mode decomposition method. Sci Total Environ 458–460: 7080. [Google Scholar]
  21. Huang J-X, 2015. Spatio-temporal analysis of malaria vectors in national malaria surveillance sites in China. Parasit Vectors 8: 146. [Google Scholar]
  22. Xia S, Xue JB, Zhang X, Hu HH, Abe EM, Rollinson D, Bergquist R, Zhou Y, Li SZ, Zhou XN, , 2017. Pattern analysis of schistosomiasis prevalence by exploring predictive modeling in Jiangling County, Hubei Province, P.R. China. Infect Dis Poverty 6: 91. [Google Scholar]
  23. Dhillon I, Guan Y, Kulis B, , 2005. A Unified View of Kernel k-means, Spectral Clustering and Graph Cuts. UTCS Technical Report #TR-04-25, 1–20.
  24. Dhillon IS, Guan Y, Kulis B, , 2004. Kernel k-means: Spectral Clustering and Normalized Cuts. The Tenth ACM SIGMOD International Conference on Knowledge Discovery and Data Mining. Seattle, WA: ACM, 551–556.
  25. Zeileis A, Hornik K, Smola A, Karatzoglou A, , 2004. kernlab-an S4 package for kernel methods in R. J Stat Softw 11: 120. [Google Scholar]
  26. Calinski T, Harabasz J, , 1974. A dendrite method for cluster analysis. Commun Stat Theory Methods 3: 127. [Google Scholar]
  27. Donghoh K, Heeseok O, , 2009. EMD: a package for empirical mode decomposition and Hilbert spectrum. R J 1: 4046. [Google Scholar]
  28. Ofulla AV, 2013. Spatial distribution and habitat characterization of schistosomiasis host snails in lake and land habitats of western Kenya. Lakes Reservoirs: Res Manage 18: 197215. [Google Scholar]
  29. Utzinger J, Zhou XN, Chen MG, Bergquist R, , 2005. Conquering schistosomiasis in China: the long march. Acta Trop 96: 6996. [Google Scholar]
  30. Zhou X-N, Wang L-Y, Chen M-G, Wang T-P, Guo J-G, Wu X-H, Jiang Q-W, Zheng J, Chen X-Y, , 2005. An economic evaluation of the national schistosomiasis control programme in China from 1992 to 2000. Acta Trop 96: 255265. [Google Scholar]
  31. Qiu J, Li RD, Xu XJ, Yu CH, Xia X, Hong XC, Chang BR, Yi FJ, Shi YY, , 2014. Identifying determinants of Oncomelania hupensis habitats and assessing the effects of environmental control strategies in the plain regions with the waterway network of China at the microscale. Int J Environ Res Public Health 11: 65716585. [Google Scholar]
  32. Su J, Lu DB, Zhou X, Wang SR, Zhuge HX, , 2013. Control efficacy of annual community-wide treatment against Schistosoma japonicum in China: a meta-analysis. PLoS One 8: e78509. [Google Scholar]
  33. Zhou X, Dandan L, Huiming Y, Honggen C, Leping S, Guojing Y, Qingbiao H, Brown L, Malone JB, , 2002. Use of landsat TM satellite surveillance data to measure the impact of the 1998 flood on snail intermediate host dispersal in the lower Yangtze River Basin. Acta Trop 82: 199205. [Google Scholar]
  34. Xianyi C, Liying W, Jiming C, Xiaonong Z, Jiang Z, Jiagang G, Xiaohua W, Engels D, Minggang C, , 2005. Schistosomiasis control in China: the impact of a 10-year World Bank Loan Project (1992–2001). Bull World Health Organ 83: 4348. [Google Scholar]
  35. Wang LD, 2009. A strategy to control transmission of Schistosoma japonicum in China. N Engl J Med 360: 121128. [Google Scholar]
  36. Longxing Q, Jing-an C, Tingting H, Fengli Y, Longzhi J, , 2014. Mathematical model of schistosomiasis under flood in Anhui province. Abstr Appl Anal 2014: 17. [Google Scholar]
  37. Xu J, Xu JF, Li SZ, Zhang LJ, Wang Q, Zhu HH, Zhou XN, , 2015. Integrated control programmes for schistosomiasis and other helminth infections in P.R. China. Acta Trop 141: 332341. [Google Scholar]
  38. Yang Y, Zhou YB, Song XX, Li SZ, Zhong B, Wang TP, Bergquist R, Zhou XN, Jiang QW, , 2016. Integrated control strategy of schistosomiasis in the People’s Republic of China: projects involving agriculture, water conservancy, forestry, sanitation and environmental modification. Adv Parasitol 92: 237268. [Google Scholar]
  39. Hong X-C, 2013. Assessing the effect of an integrated control strategy for schistosomiasis japonica emphasizing bovines in a marshland area of Hubei Province, China: a cluster randomized trial. PLoS Negl Trop Dis 7: e2122. [Google Scholar]
  40. Sun LP, Wang W, Liang YS, Tian ZX, Hong QB, Yang K, Yang GJ, Dai JR, Gao Y, , 2011. Effect of an integrated control strategy for schistosomiasis japonica in the lower reaches of the Yangtze River, China: an evaluation from 2005 to 2008. Parasit Vectors 4: 243. [Google Scholar]
  41. Yu D, Cheng J, Yang Y, , 2005. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mech Syst Signal Process 19: 259270. [Google Scholar]
  42. Huang NE, Wu ML, Qu WD, Long SR, Shen S, Zhang JE, , 2003. Applications of Hilbert-Huang transform to non-stationary financial time series analysis. Appl Stochastic Models Data Anal 19: 361. [Google Scholar]
  43. Wang XH, Wu XH, Zhou XN, , 2006. Bayesian estimation of community prevalences of Schistosoma japonicum infection in China. Int J Parasitol 36: 895902. [Google Scholar]
  44. Wang XH, Zhou XN, Vounatsou P, Chen Z, Utzinger J, Yang K, Steinmann P, Wu XH, , 2008. Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic ‘gold’ standard. PLoS Negl Trop Dis 2: e250. [Google Scholar]

Data & Media loading...

Supplemental Information

  • Received : 16 Jun 2017
  • Accepted : 26 Dec 2017
  • Published online : 12 Feb 2018

Most Cited This Month

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error