Factors influencing U.S. canine heartworm (Dirofilaria immitis) prevalence
AffiliationDepartment of Mathematical Sciences, Clemson University, Clemson, SC 29634-0975, USA
Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
Division of Epidemiology and Biostatistics, College of Public Health, University of Arizona, Tucson, AZ 85724, USA
Department of Entomology, Cornell University, Ithaca, NY 14853, USA
Entomology and Nematology Department, University of Florida, Gainesville, FL 32611, USA
Department of Biological Sciences, Arkansas State University, State University, AR 72467, USA
Animal Medical Center, Anniston, AL 36201, USA
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CitationWang et al. Parasites & Vectors 2014, 7:264 http://www.parasitesandvectors.com/content/7/1/264
JournalParasites & Vectors
Rights© 2014 Wang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)
Collection InformationThis item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at email@example.com.
AbstractBACKGROUND:This paper examines the individual factors that influence prevalence rates of canine heartworm in the contiguous United States. A data set provided by the Companion Animal Parasite Council, which contains county-by-county results of over nine million heartworm tests conducted during 2011 and 2012, is analyzed for predictive structure. The goal is to identify the factors that are important in predicting high canine heartworm prevalence rates.METHODS:The factors considered in this study are those envisioned to impact whether a dog is likely to have heartworm. The factors include climate conditions (annual temperature, precipitation, and relative humidity), socio-economic conditions (population density, household income), local topography (surface water and forestation coverage, elevation), and vector presence (several mosquito species). A baseline heartworm prevalence map is constructed using estimated proportions of positive tests in each county of the United States. A smoothing algorithm is employed to remove localized small-scale variation and highlight large-scale structures of the prevalence rates. Logistic regression is used to identify significant factors for predicting heartworm prevalence.RESULTS:All of the examined factors have power in predicting heartworm prevalence, including median household income, annual temperature, county elevation, and presence of the mosquitoes Aedes trivittatus, Aedes sierrensis and Culex quinquefasciatus. Interactions among factors also exist.CONCLUSIONS:The factors identified are significant in predicting heartworm prevalence. The factor list is likely incomplete due to data deficiencies. For example, coyotes and feral dogs are known reservoirs of heartworm infection. Unfortunately, no complete data of their populations were available. The regression model considered is currently being explored to forecast future values of heartworm prevalence.
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