Climate-based risk models for Fasciola hepatica in Colombia

Submitted: 17 December 2014
Accepted: 17 December 2014
Published: 1 September 2012
Abstract Views: 2451
PDF: 1155
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A predictive Fasciola hepatica model, based on the growing degree day-water budget (GDD-WB) concept and the known biological requirements of the parasite, was developed within a geographical information system (GIS) in Colombia. Climate-based forecast index (CFI) values were calculated and represented in a national-scale, climate grid (18 x 18 km) using ArcGIS 9.3. A mask overlay was used to exclude unsuitable areas where mean annual temperature exceeded 25 °C, the upper threshold for development and propagation of the F. hepatica life cycle. The model was then validated and further developed by studies limited to one department in northwest Colombia. F. hepatica prevalence data was obtained from a 2008-2010 survey in 10 municipalities of 6,016 dairy cattle at 673 herd study sites, for which global positioning system coordinates were recorded. The CFI map results were compared to F. hepatica environmental risk models for the survey data points that had over 5% prevalence (231 of the 673 sites) at the 1 km2 scale using two independent approaches: (i) a GIS map query based on satellite data parameters including elevation, enhanced vegetation index and land surface temperature day-night difference; and (ii) an ecological niche model (MaxEnt), for which geographic point coordinates of F. hepatica survey farms were used with BioClim data as environmental variables to develop a probability map. The predicted risk pattern of both approaches was similar to that seen in the forecast index grid. The temporal risk, evaluated by the monthly CFIs and a daily GDD-WB forecast software for 2007 and 2008, revealed a major July-August to January transmission period with considerable inter-annual differences.



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How to Cite

Valencia-Lòpez, N., Malone, J. B., Gòmez Carmona, C., & Velásquez, L. E. (2012). Climate-based risk models for Fasciola hepatica in Colombia. Geospatial Health, 6(3), S75-S85.

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