Informing Surveillance through the Characterization of Outbreak Potential of Chronic Wasting Disease in White-Tailed Deer

Hanley, Brenda J.; Carstensen, Michelle; Walsh, Daniel P.; Christensen, Sonja A.; Storm, Daniel J.; Booth, James G.; Guinness, Joseph; Them, Cara E.; Ahmed, Md Sohel; Schuler, Krysten L.

doi:10.1016/j.ecolmodel.2022.110054

Cited by 5 publications

(4 citation statements)

References 54 publications

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“…While agencies may already be searching for CWD in areas contiguous to core infections, the CWD Prediction Web App may be particularly helpful in illuminating counties vulnerable to CWD in non-obvious places. In noncontiguous counties predicted by the CWD Prediction Web App to be CWD-positive, we suggest using the CWD Prediction Web App in conjunction with other models that pinpoint conditions for in situ outbreaks 7 , 51 , 52 for surveillance planning. In addition to the error reductions recommended above, we recommend that future ML models better characterize the spread of disease across the landscape by incorporating geographical proximity data or information from diffusion models 53 which we did not do.…”

Section: Discussionmentioning

confidence: 99%

“…Such programs focus on identifying locations most likely to harbor CWD and provide the best opportunity to manage the disease while prevalence is low 5 ; however, these programs constitute an enormous monetary and human resource cost to agencies 6 . Accordingly, post hoc evaluation of existing surveillance data has focused on pinpointing variables in association with the emergence and spread of CWD to further inform the next year of surveillance 7 .…”

Section: Introductionmentioning

confidence: 99%

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Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning

Ahmed,

Hanley,

Mitchell

et al. 2024

Sci Rep

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Continued spread of chronic wasting disease (CWD) through wild cervid herds negatively impacts populations, erodes wildlife conservation, drains resource dollars, and challenges wildlife management agencies. Risk factors for CWD have been investigated at state scales, but a regional model to predict locations of new infections can guide increasingly efficient surveillance efforts. We predicted CWD incidence by county using CWD surveillance data depicting white-tailed deer (Odocoileus virginianus) in 16 eastern and midwestern US states. We predicted the binary outcome of CWD-status using four machine learning models, utilized five-fold cross-validation and grid search to pinpoint the best model, then compared model predictions against the subsequent year of surveillance data. Cross validation revealed that the Light Boosting Gradient model was the most reliable predictor given the regional data. The predictive model could be helpful for surveillance planning. Predictions of false positives emphasize areas that warrant targeted CWD surveillance because of similar conditions with counties known to harbor CWD. However, disagreements in positives and negatives between the CWD Prediction Web App predictions and the on-the-ground surveillance data one year later underscore the need for state wildlife agency professionals to use a layered modeling approach to ensure robust surveillance planning. The CWD Prediction Web App is at https://cwd-predict.streamlit.app/.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning

Ahmed,

Hanley,

Mitchell

et al. 2024

Sci Rep

Self Cite

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“…For the analyses of landscapes in a continuous‐time framework (vs. a patch occupancy framework in discrete time), ecological diffusion models have also been used to estimate the spread of pathogens at large spatial scales (e.g., Hefley et al, 2017; Wu, 2008). These models can use detection/non‐detection data from surveillance programs to estimate the effects of covariates on pathogen growth and spread, and have been used to forecast disease occurrence dynamics, and to develop targeted surveillance programs (e.g., by identifying the leading edge of pathogen invasion; Hanley et al, 2022).…”

Section: Frequently Identified Disease Management Objectivesmentioning

confidence: 99%

Matching decision support modeling frameworks to disease emergence stages and associated management objectives

Grant,

Mosher,

Bernard

et al. 2024

Conservat Sci and Prac

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Wildlife disease management decisions often require rapid responses to situations that are fraught with uncertainty. By recognizing that management is implemented to achieve specific objectives, resource managers and science partners can identify an analysis technique and develop a monitoring plan to evaluate management effectiveness. For emerging infectious diseases, objectives may take several distinct forms, dependent on the perceived stage of disease emergence (i.e., pre‐epidemic, early outbreak, mid‐epidemic, and endemic), the expected rate of spread, and the anticipated effect of the disease on host populations. Identifying modeling techniques and metrics that are linked to management objectives will require early and consistent communication between managers and science partners. We link modeling approaches that can be used to forecast and evaluate the performance of intervention strategies with a range of disease management objectives. Our aim is to help scientists recognize alternative modeling approaches which may better align with different forms of disease management objectives, and to help managers evaluate the relevance of proposed modeling approaches to their specified objectives for disease management. Recognizing that disease management objectives can take different forms, and thus require different modeling approaches, can help wildlife disease response teams (i.e., natural resource managers, scientists, and stakeholders working collaboratively) better prepare and respond to disease threats.

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“…Chronic wasting disease is difficult to eliminate from wild populations since the amount of infectious prions in the environment increases with the number of infectious animals (Gross & Miller, 2001 ; Manjerovic et al., 2014 ). Therefore, management tends to be less effective the longer the disease has progressed, and adequate data on underlying factors are important for implementing effective management (Hanley et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Large‐scale assessment of genetic structure to assess risk of populations of a large herbivore to disease

Walter,

Fameli,

Russo‐Petrick

et al. 2024

Ecology and Evolution

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Chronic wasting disease (CWD) can spread among cervids by direct and indirect transmission, the former being more likely in emerging areas. Identifying subpopulations allows the delineation of focal areas to target for intervention. We aimed to assess the population structure of white‐tailed deer (Odocoileus virginianus) in the northeastern United States at a regional scale to inform managers regarding gene flow throughout the region. We genotyped 10 microsatellites in 5701 wild deer samples from Maryland, New York, Ohio, Pennsylvania, and Virginia. We evaluated the distribution of genetic variability through spatial principal component analysis and inferred genetic structure using non‐spatial and spatial Bayesian clustering algorithms (BCAs). We simulated populations representing each inferred wild cluster, wild deer in each state and each physiographic province, total wild population, and a captive population. We conducted genetic assignment tests using these potential sources, calculating the probability of samples being correctly assigned to their origin. Non‐spatial BCA identified two clusters across the region, while spatial BCA suggested a maximum of nine clusters. Assignment tests correctly placed deer into captive or wild origin in most cases (94%), as previously reported, but performance varied when assigning wild deer to more specific origins. Assignments to clusters inferred via non‐spatial BCA performed well, but efficiency was greatly reduced when assigning samples to clusters inferred via spatial BCA. Differences between spatial BCA clusters are not strong enough to make assignment tests a reliable method for inferring the geographic origin of deer using 10 microsatellites. However, the genetic distinction between clusters may indicate natural and anthropogenic barriers of interest for management.

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Informing Surveillance through the Characterization of Outbreak Potential of Chronic Wasting Disease in White-Tailed Deer

Cited by 5 publications

References 54 publications

Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning

Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning

Matching decision support modeling frameworks to disease emergence stages and associated management objectives

Large‐scale assessment of genetic structure to assess risk of populations of a large herbivore to disease

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