A Spatial-Temporal Model for Assessing the Effects of Intervillage Connectivity in Schistosomiasis Transmission

  1. Bing Xu1,
  2. Peng Gong2,
  3. Edmund Seto3,
  4. Song Liang3,
  5. Changhong Yang4,
  6. Song Wen4,
  7. Dongchuan Qiu4,
  8. Xueguang Gu4,
  9. Robert Spear3

Article first published online: 9 MAR 2006

DOI: 10.1111/j.1467-8306.2006.00497.x

Issue

Annals of the Association of American Geographers

Annals of the Association of American Geographers

Volume 96, Issue 1, pages 31–46, March 2006

Additional Information

How to Cite

Xu, B., Gong, P., Seto, E., Liang, S., Yang, C., Wen, S., Qiu, D., Gu, X. and Spear, R. (2006), A Spatial-Temporal Model for Assessing the Effects of Intervillage Connectivity in Schistosomiasis Transmission. Annals of the Association of American Geographers, 96: 31–46. doi: 10.1111/j.1467-8306.2006.00497.x

Author Information

  1. 1

    Center for Assessment and Monitoring of Forest and Environmental Resources, University of California, Berkeley, and Department of Geography, University of Utah

  2. 2

    Center for Assessment and Monitoring of Forest and Environmental Resources, University of California, Berkeley, and State Key Lab of Remote Sensing Science, China

  3. 3

    Center for Occupational and Environmental Health, School of Public Health, University of California, Berkeley

  4. 4

    Sichuan Institute of Parasitic Diseases, China

*Center for Natural and Technological Hazards, Department of Geography, University of Utah, 260 S. Central Campus Dr., Rm. 270, Salt Lake City, UT 84112-9155, e-mail: bing.xu@geog.utah.edu (Xu); Center for Assessment and Monitoring of Forest and Environmental Resources (CAMFER), University of California, Berkeley, 151 Hilgard Hall, Berkeley, CA 94720-3110, e-mail: gong@nature.berkeley.edu (Gong); Center for Occupational and Environmental Health, School of Public Health, University of California, Berkeley, 140 Earl Warren Hall, #7360, Berkeley, CA 94720-7360, e-mail: seto@berkeley.edu (Seto), liang1@berkeley.edu (Liang), spear@berkeley.edu(Spear); Sichuan Institute of Parasitic Diseases, 10 University Road, Chengdu, Sichuan 610041, P. R. China (Gu), e-mail: changhongyang@gmail.com (Yang), wenzi5411@hotmail.com (Wen), qiudongchuan@hotmail.com (Qiu).

Publication History

  1. Issue published online: 9 MAR 2006
  2. Article first published online: 9 MAR 2006
  3. Initial submission, April 2004; revised submission, August 2004; final acceptance, November 2004

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Keywords:

  • geographic information systems;
  • remote sensing;
  • schistosomiasis;
  • spatial interaction;
  • spatial-temporal model

Spatial interaction and connectivity are important factors in the spread of infectious diseases. We developed a spatial-temporal model of schistosomiasis japonica transmission, a disease caused by parasites that are transported via surface water and that live in both snail and human hosts. The model employed a spatial interaction matrix based on neighborhood relationships and hydrologic connectivity to assess the effect of intervillage parasitic transport on disease transmission and control. Satellite remote-sensing data served as input to the model for predicting snail density within each village, and for deriving a digital elevation model that was used to quantify hydrologic connectivity. Simulations of the model with varying levels of connectivity and in the presence and absence of chemotherapy control were run for 227 villages near Xichang City, in southwest Sichuan province, China. Increasing connectivity resulted in a geographic clustering of parasites within particular villages that produced higher levels of worm burden than in low and no connectivity simulations. Worm burden within a village could either increase or decrease with connectivity, depending on the degree to which parasites were imported and exported. Simulations of mass chemotherapy in select villages can result in a beneficial reduction in worm burden that extends to downstream neighbors. These findings suggest that better understanding of intervillage connectedness can be exploited in the design of cost-effective control strategies.

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