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Predicting the odds of chronic wasting disease with Habitat Risk software.

Authors :
Walter WD
Hanley B
Them CE
Mitchell CI
Kelly J
Grove D
Hollingshead N
Abbott RC
Schuler KL
Source :
Spatial and spatio-temporal epidemiology [Spat Spatiotemporal Epidemiol] 2024 Jun; Vol. 49, pp. 100650. Date of Electronic Publication: 2024 Apr 11.
Publication Year :
2024

Abstract

Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in captive cervids in Colorado, United States (US) in 1967, but has since spread into free-ranging white-tailed deer (Odocoileus virginianus) across the US and Canada as well as to Scandinavia and South Korea. In some areas, the disease is considered endemic in wild deer populations, and governmental wildlife agencies have employed epidemiological models to understand long-term environmental risk. However, continued rapid spread of CWD into new regions of the continent has underscored the need for extension of these models into broader tools applicable for wide use by wildlife agencies. Additionally, efforts to semi-automate models will facilitate access of technical scientific methods to broader users. We introduce software (Habitat Risk) designed to link a previously published epidemiological model with spatially referenced environmental and disease testing data to enable agency personnel to make up-to-date, localized, data-driven predictions regarding the odds of CWD detection in surrounding areas after an outbreak is discovered. Habitat Risk requires pre-processing publicly available environmental datasets and standardization of disease testing (surveillance) data, after which an autonomous computational workflow terminates in a user interface that displays an interactive map of disease risk. We demonstrated the use of the Habitat Risk software with surveillance data of white-tailed deer from Tennessee, USA.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Published by Elsevier Ltd.)

Details

Language :
English
ISSN :
1877-5853
Volume :
49
Database :
MEDLINE
Journal :
Spatial and spatio-temporal epidemiology
Publication Type :
Academic Journal
Accession number :
38876563
Full Text :
https://doi.org/10.1016/j.sste.2024.100650