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Nevin Manimala Statistics

COMPARISON OF TWO SURVEILLANCE COMPONENTS FOR INVESTIGATING THE EPIDEMIOLOGY OF CANINE DISTEMPER VIRUS IN RACCOONS (PROCYON LOTOR)

J Wildl Dis. 2021 Jan 6;57(1):104-115. doi: 10.7589/JWD-D-19-00001.

ABSTRACT

Canine distemper virus (CDV) has a broad mammalian host range. In Ontario, Canada, CDV is frequently encountered in wild carnivores and is the most common infectious cause of death for raccoons (Procyon lotor). The isolation of wild-type CDV strains genetically distinct from vaccine strains in North America has renewed interest in the epidemiological patterns of this virus. However, wildlife surveillance is challenging and often utilizes a combination of surveillance methods with aggregation of data from multiple sources. Our objective was to compare raccoon CDV data generated through two separate surveillance components operated by the Ontario-Nunavut node of the Canadian Wildlife Health Cooperative. The raw data generated by each component in addition to the results of multilevel logistic regression and spatial scan statistics, were compared between the datasets. A total of 498 raccoons obtained via passive surveillance between 2007 and 2017 and 887 raccoons obtained via enhanced-passive surveillance between 2014 and 2017, were tested for CDV. The number and geographic distribution of reports, proportion of yearly reports classified as CDV-positive, and characteristics of CDV-positive raccoons differed between passive and enhanced-passive surveillance components. Geographical data demonstrated that CDV infection was present throughout southern Ontario. The geographic area of both surveillance components combined was more representative than either passive or enhanced-passive surveillance in isolation; but was restricted compared to the overall distribution of raccoons in Ontario. Regression analyses produced statistically significant associations between the presence of CDV and host and environmental variables that were at times discordant between the two datasets. Studying the properties of these datasets will inform future passive wildlife surveillance strategies and highlights the impact that a surveillance strategy can have on the results of epidemiological analyses.

PMID:33635985 | DOI:10.7589/JWD-D-19-00001

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