https://doi.org/10.4081/gh.2012.119

SandflyMap: leveraging spatial data on sand fly vector distribution for disease risk assessments

Vol. 6 No. 3 (2012)
Submitted: 17 December 2014
Accepted: 17 December 2014
Published: 1 September 2012
database, distribution, SandflyMap, VectorMap, leishmaniasis, georeferencing, geographical information system.
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Authors

Desmond H. Foley
foleydes@si.edu
Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States.
Richard C. Wilkerson
Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States.
L. Lynnette Dornak
Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States.
David B. Pecor
Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States.
Arpad S. Nyari
Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States.
Leopoldo M. Rueda
Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States.
Lewis S. Long
Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States.
Jason H. Richardson
Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States.
We feature SandflyMap (www.sandflymap.org), a new map service within VectorMap (www.vectormap.org) that allows free public online access to global sand fly, tick and mosquito collection records and habitat suitability models. Given the short home range of sand flies, combining remote sensing and collection point data give a powerful insight into the environmental determinants of sand fly distribution. SandflyMap is aimed at medical entomologists, vector disease control workers, public health officials and health planners. Data are checked for geographical and taxonomic errors, and are comprised of vouchered specimen information, and both published and unpublished observation data. SandflyMap uses Microsoft Silverlight and ESRI's ArcGIS Server 10 software platform to present disease vector data and relevant remote sensing layers in an online geographical information system format. Users can view the locations of past vector collections and the results of models that predict the geographic extent of individual species. Collection records are searchable and downloadable, and Excel collection forms with drop down lists, and Excel charts to country, are available for data contributors to map and quality control their data. SandflyMap makes accessible, and adds value to, the results of past sand fly collecting efforts. We detail the workflow for entering occurrence data from the literature to SandflyMap, using an example for sand flies from South America. We discuss the utility of SandflyMap as a focal point to increase collaboration and to explore the nexus between geography and vector-borne disease transmission.

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How to Cite

Foley, D. H., Wilkerson, R. C., Dornak, L. L., Pecor, D. B., Nyari, A. S., Rueda, L. M., Long, L. S., & Richardson, J. H. (2012). SandflyMap: leveraging spatial data on sand fly vector distribution for disease risk assessments. Geospatial Health, 6(3), S25-S30. https://doi.org/10.4081/gh.2012.119

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