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A Landscape-based Model for Predicting Mycobacterium ulcerans Infection (Buruli Ulcer Disease) Presence in Benin, West Africa

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Abstract

Mycobacterium ulcerans infection (Buruli ulcer [BU] disease) is an emerging tropical disease that causes severe morbidity in many communities, especially those in close proximity to aquatic environments. Research and control efforts are severely hampered by the paucity of data regarding the ecology of this disease; for example, the vectors and modes of transmission remain unknown. It is hypothesized that BU presence is associated with altered landscapes that perturb aquatic ecosystems; however, this has yet to be quantified over large spatial scales. We quantified relationships between land use/land cover (LULC) characteristics surrounding individual villages and BU presence in Benin, West Africa. We also examined the effects of other village-level characteristics which we hypothesized to affect BU presence, such as village distance to the nearest river. We found that as the percent urban land use in a 50-km buffer surrounding a village increased, the probability of BU presence decreased. Conversely, as the percent agricultural land use in a 20-km buffer surrounding a village increased, the probability of BU presence increased. Landscape-based models had predictive ability when predicting BU presence using validation data sets from Benin and Ghana, West Africa. Our analyses suggest that relatively small amounts of urbanization are associated with a decrease in the probability of BU presence, and we hypothesize that this is due to the increased availability of pumped water in urban environments. Our models provide an initial approach to predicting the probability of BU presence over large spatial scales in Benin and Ghana, using readily available land use data.

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Acknowledgments

The authors acknowledge and thank K. Asiedu of the World Health Organization and E. Ampadu of the Ghana Ministry of Health for providing Buruli ulcer case data and R. Kolar for managing the Buruli ulcer case data. We also thank Dr. L. Waller for helpful comments on an earlier draft of this manuscript. This work was funded by NIH grants 1R01 TW007550-01 and 1R03 AI062719-01A1, the former an award through Fogarty International Center and the NIH/NSF Ecology of Infectious Diseases Program. This is publication 2007-08 of the Quantitative Fisheries Center.

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Authors and Affiliations

  1. Quantitative Fisheries Center, Fisheries and Wildlife, Michigan State University, East Lansing, MI, 48824, USA

    Tyler Wagner

  2. Department of Entomology, Michigan State University, East Lansing, MI, 48824, USA

    M. Eric Benbow

  3. Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, 48824, USA

    Meghan Burns & Jiaguo Qi

  4. Programme National de lutte contre l’UB, Cotonou, Benin, West Africa

    R. Christian Johnson

  5. Departments of Entomology and Fisheries and Wildlife, Michigan State University, East Lansing, MI, 48824, USA

    Richard W. Merritt

  6. Department of Microbiology, University of Tennessee, Knoxville, TN, 37996, USA

    Pamela L. C. Small

Authors
  1. Tyler Wagner
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  2. M. Eric Benbow
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  3. Meghan Burns
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  4. R. Christian Johnson
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  5. Richard W. Merritt
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  6. Jiaguo Qi
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  7. Pamela L. C. Small
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Correspondence to Tyler Wagner.

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Wagner, T., Benbow, M.E., Burns, M. et al. A Landscape-based Model for Predicting Mycobacterium ulcerans Infection (Buruli Ulcer Disease) Presence in Benin, West Africa. EcoHealth 5, 69–79 (2008). https://doi.org/10.1007/s10393-007-0148-7

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  • DOI: https://doi.org/10.1007/s10393-007-0148-7

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