Since its detection in the late 1960s in Colorado, chronic wasting disease (CWD) has affected thousands of cervids in 20 U.S. states and 2 Canadian provinces, and was recently detected in Europe. Understanding exactly how the disease is likely to spread between populations is critical in being able to stop it from infecting more individuals.
New research has challenged the traditional method of predicting the spread of CWD by measuring Euclidean distance to the disease source. Although this method is useful for wild populations where the driver of host movement may be unclear, it ignores the ways that landscape connectivity can direct individual movement and therefore disease movement. A new study by Norbert et al. predicts CWD occurrence by using a landscape connectivity metric quantified by step selection function modelling and circuit theory. Models incorporated movement data that was collected from mule deer and white-tailed deer in central Canada since 2000.
The study shows that considering landscape connectivity metrics in spatial patterns of disease spread allows for a much more accurate picture than when using Euclidean distances. High connectivity was associated with a high risk of CWD, with the highest risk found in areas with dense streams and abundant agriculture where herds are likely to congregate or be funneled. The authors recommend that landscape connectivity should be considered among more complex, temporal models to better understand how the disease might spread. By doing so, managers can not only improve disease surveillance programs, but also increase the likelihood of early detection.