Use of deep learning to predict chronic wasting disease status based on animal movement.

Raymond L Blaha, Christopher J Silva, Stephanie A Cunningham, Melia T DeVivo, David R Edmunds, Melanie R Boudreau

Abstract read

In one paragraph

Article in Movement ecology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Raymond L BlahaDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi State, MS, 39762, USA.

Christopher J SilvaUnited States Department of Agriculture, Agriculture Research Service, Western Regional Research Center, Albany, CA, 94710, USA.

Stephanie A CunninghamDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi State, MS, 39762, USA.

Melia T DeVivoDepartment of Veterinary Sciences, University of Wyoming, Laramie, WY, 82070, USA.

David R EdmundsDepartment of Veterinary Sciences, University of Wyoming, Laramie, WY, 82070, USA.

Melanie R BoudreauDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi State, MS, 39762, USA. mel.r.boudreau@gmail.com.

Funding

CRIS project 2030-32000-011-000-DU.S. Department of Agriculture, Agricultural Research Service number 58-6064-3-017

6 · The paper itself

Abstract

Chronic Wasting Disease (CWD) is an invariably fatal prion disease that impacts cervid populations and wildlife management across North America. Infected cervids often remain asymptomatic for months and movement-based anomaly detection from Global Positioning System collaring data offers a potential tool for understanding early and late stage CWD-based behavioral changes. Here we evaluate whether deep learning behavioral anomaly detection models such as autoencoders (AE) and conditional autoencoders (cAE) can effectively identify anomalous movement changes in free-ranging mule deer (Odocoileus hemionus) that may have been associated with CWD infection. Unsupervised AEs achieved ≥ 84% accuracy and 89% precision irrespective of whether the model was trained using only CWD

Indexed as

AutoencodersChronic wasting diseaseConditional autoencodersMachine learningMule deerOdocoileus hemionusWyoming

Identifiers

PMID42271470

DOI10.1186/s40462-026-00668-4

PMCPMC13479571

What OpenQuestion holds

Textmetadata

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.