Surveillance and epidemic modeling were used to study chronic wasting disease (CWD), a transmissible spongiform encephalopathy that occurs naturally among sympatric, free-ranging deer (Odocoileus spp.) and Rocky Mountain elk (Cervus elaphus nelsoni) populations in contiguous portions of northeastern Colorado and southeastern Wyoming (USA). We used clinical case submissions to identify endemic areas, then used immunohistochemistry to detect CWD-infected individuals among 5,513 deer and elk sampled via geographically-focused random surveys. Estimated overall prevalence (prevalence, 95% confidence interval) in mule deer (4.9%, 4.1 to 5.7%) was higher than in white-tailed deer (2.1%, 0.5 to 3.4%) or elk (0.5%, 0.001 to 1%) in endemic areas; CWD was not detected in outlying portions of either state. Within species, CWD prevalence varied widely among biologically- or geographically-segregated subpopulations within the 38,137 km2 endemic area but appeared stable over a 3-yr period. The number of clinical CWD cases submitted from an area was a poor predictor of local CWD prevalence, and prevalence was typically > or =1% before clinical cases were first detected in most areas. Under plausible transmission assumptions that mimicked field data, prevalence in epidemic models reached about 1% in 15 to 20 yr and about 15% in 37 to 50 yr. Models forecast population declines once prevalence exceeded about 5%. Both field and model data supported the importance of lateral transmission in CWD dynamics. Based on prevalence, spatial distribution, and modeling, we suggest CWD has been occurring in northeastern Colorado and southeastern Wyoming for >30 yr, and may be best represented as an epizootic with a protracted time-scale.

Diseased animals may exhibit behavioral shifts that increase or decrease their probability of being randomly sampled. In harvest-based sampling approaches, animal movements, changes in habitat utilization, changes in breeding behaviors during harvest periods, or differential susceptibility to harvest via behaviors like hiding or decreased sensitivity to stimuli may result in a non-random sample that biases prevalence estimates. We present a method that can be used to determine whether bias exists in prevalence estimates from harvest samples. Using data from harvested mule deer (Odocoileus hemionus) sampled in northcentral Colorado (USA) during fall hunting seasons 1996-98 and Akaike's information criterion (AIC) model selection, we detected within-yr trends indicating potential bias in harvest-based prevalence estimates for chronic wasting disease (CWD). The proportion of CWD-positive deer harvested slightly increased through time within a yr. We speculate that differential susceptibility to harvest or breeding season movements may explain the positive trend in proportion of CWD-positive deer harvested during fall hunting seasons. Detection of bias may provide information about temporal patterns of a disease, suggest biological hypotheses that could further understanding of a disease, or provide wildlife managers with information about when diseased animals are more or less likely to be harvested. Although AIC model selection can be useful for detecting bias in data, it has limited utility in determining underlying causes of bias. In cases where bias is detected in data using such model selection methods, then design-based methods (i.e., experimental manipulation) may be necessary to assign causality.

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