Abstract
Many infectious diseases in wildlife occur under quantifiable landscape ecological patterns useful in facilitating epidemiological surveillance and management, though little is known about prion diseases. Chronic wasting disease (CWD), a fatal prion disease of the deer family Cervidae, currently affects white-tailed deer (Odocoileus virginianus) populations in the Mid-Atlantic United States (US) and challenges wildlife veterinarians and disease ecologists from its unclear mechanisms and associations within landscapes, particularly in early phases of an outbreak when CWD detections are sparse. We aimed to provide guidance for wildlife disease management by identifying the extent to which CWD-positive cases can be reliably predicted from landscape conditions. Using the CWD outbreak in Virginia, US from 2009 to early 2020 as a case study system, we used diverse algorithms (e.g., principal components analysis, support vector machines, kernel density estimation) and data partitioning methods to quantify remotely sensed landscape conditions associated with CWD cases. We used various model evaluation tools (e.g., AUC ratios, cumulative binomial testing, Jaccard similarity) to assess predictions of disease transmission risk using independent CWD data. We further examined model variation in the context of uncertainty. We provided significant support that vegetation phenology data representing landscape conditions can predict and map CWD transmission risk. Model predictions improved when incorporating inferred home ranges instead of raw hunter-reported coordinates. Different data availability scenarios identified variation among models. By showing that CWD could be predicted and mapped, our project adds to the available tools for understanding the landscape ecology of CWD transmission risk in free-ranging populations and natural conditions. Our modeling framework and use of widely available landscape data foster replicability for other wildlife diseases and study areas.
Highlights
Effective wildlife disease management and control depends upon epidemiological surveillance, though identifying geographic locations where surveillance should be deployed can be challenging and require extensive sampling regimes [1]
Upon evaluation of the algorithms used in our black-box framework, we found that both kernel density estimation (KDE) and support vector machines (SVM) algorithms generated statistically significant predictions of Chronic wasting disease (CWD) cases according to cumulative binomial probability testing (Table 1)
Models were statistically significant at both scales despite the proportion of area projected as suitable being higher in hypervolumes delineated from Harvest Locations (Table 1)
Summary
Effective wildlife disease management and control depends upon epidemiological surveillance, though identifying geographic locations where surveillance should be deployed can be challenging and require extensive sampling regimes [1]. Prions are a group of infectious pathogens that cause neurodegenerative diseases in humans and animals [7]. This perceived lag may be the result, at least in part, to unclear origins of prion biology and the atypical biological properties of prions with respect to other pathogens [8]. Because of their inextricable connection with hosts and the unclear role of other animals in their propagation, prion diseases remain a unique challenge in wildlife epidemiology [9]
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