4.1 Article

SURVEILLANCE TO DETECT CHRONIC WASTING DISEASE IN WHITE-TAILED DEER IN WISCONSIN

Journal

JOURNAL OF WILDLIFE DISEASES
Volume 45, Issue 4, Pages 989-997

Publisher

WILDLIFE DISEASE ASSOC, INC
DOI: 10.7589/0090-3558-45.4.989

Keywords

Chronic wasting disease; disease surveillance; Odocoileus virginianus; white-tailed deer; Wisconsin

Funding

  1. US Geological Survey-National Wildlife Health Center
  2. Wisconsin Department of Natural Resources

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Chronic wasting disease (CWD), a prion disease affecting North American cervids, has been discovered in at least 12 states and provinces throughout the continent. Since 2002, a number of states and provinces have initiated surveillance programs to detect CWD in native cervid populations. However, many questions remain about the appropriate methods, geographic scope, and number of samples required for all effective CWD surveillance program. We provide,in improved statistical method to calculate the probability of detecting CWD:) in primary sample units (e.g., county or deer management unit) that also considers deer abundance and the nonrandom distribution of CWD and hunter harvests. We used this method to analyze data from a statewide CWD detection program conducted in Wisconsin during the autumns of 2002 and 2003 to determine the distribution Of CWD ill white-tailed deer (Odocoileus virginianus). Deer heads were collected at hunter registration stations, and brainstem (obex) and retropharyngeal lymph nodes were removed for disease testing. Our analysis includes samples from >35,000 deer collected outside the known affected area. The probability of detecting chronic wasting disease at a prevalence of 1% varied from 0.89 to >= 0.99 among the 56 primary sample units. Detection probabilities for 1% CWD prevalence were >0.9 in 55 primary sample units, and >0.99 in 10. Detection probabilities will he higher in areas where CWD prevalence exceeds 1%. CWD-positive deer were detected in eight primary sample units surrounding the known affected area during surveillance activities. Our approach provides a novel statistical technique to accommodate nonrandom sampling in wildlife disease surveillance programs.

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