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Rapid, Seamless Generation of Recombinant Poxviruses using Host Range and Visual Selection
Published on: May 24, 2020
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Viral genomic features predict Orthopoxvirus reservoir hosts
Katie K Tseng1, Heather Koehler2, Daniel J Becker3
1Paul G. Allen School for Global Health, Washington State University, Pullman, Washington, United States of America.
Biorxiv : the Preprint Server for Biology
|November 14, 2023
Summary
Machine learning models predict Orthopoxviruses (OPVs) reservoirs by combining host and viral data. This identifies high-risk regions for zoonotic OPV emergence, guiding wildlife surveillance efforts.
Area of Science:
- Virology
- Epidemiology
- Machine Learning
Background:
- Orthopoxviruses (OPVs), including smallpox and mpox viruses, cause significant global outbreaks.
- Declining smallpox vaccination rates have reduced population immunity to OPVs.
Purpose of the Study:
- To predict potential reservoirs of OPVs using machine learning.
- To identify geographic regions at heightened risk for zoonotic OPV emergence.
Main Methods:
- Employed machine learning models integrating host ecological and viral genomic features.
- Validated the enhanced predictive accuracy of models incorporating viral genomic data.
Main Results:
- Viral genomic features significantly improved the accuracy of predicting OPV host species.
- Identified hotspots for potential OPV hosts in Southeast Asia, equatorial Africa, and the Amazon.
- High overlap exists between predicted OPV host regions and areas with low smallpox vaccination coverage.
Conclusions:
- Host-virus molecular interactions are crucial for predicting zoonotic spillover.
- Findings highlight increased risk for zoonotic OPV emergence in specific global regions.
- Results can inform targeted wildlife surveillance for mpox and other OPVs.
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