Focusing Viral Risk Ranking Tool on Prediction
Katherine Budeski, Marc Lipsitch

TL;DR
This study reanalyzed the Viral Risk Ranking tool by removing spillover-dependent factors, revealing that the original tool heavily relied on these factors for predicting zoonotic spillover risk, and suggesting improvements for future versions.
Contribution
The paper demonstrates that excluding spillover-dependent factors reduces the predictive accuracy of the Viral Risk Ranking tool, highlighting the need for non-spillover factors in risk assessment.
Findings
Original ranking scores correlated with high spillover risk.
Removing spillover-dependent factors decreased predictive accuracy.
Most spillover-dependent factors differed between human and non-human viruses.
Abstract
Preparing to rapidly respond to emerging infectious diseases is becoming ever more critical. "SpillOver: Viral Risk Ranking" is an open-source tool developed to evaluate novel wildlife-origin viruses for their risk of spillover from animals to humans and their risk of spreading in human populations. However, several of the factors used in the risk assessment are dependent on evidence of previous zoonotic spillover and/or sustained transmission in humans. Therefore, we performed a reanalysis of the "Ranking Comparison" after removing eight factors that require post-spillover knowledge and compared the adjusted risk rankings to the originals. The top 10 viruses as ranked by their adjusted scores also had very high original scores. However, the predictive power of the tool for whether a virus was a human virus or not as classified in the Spillover database deteriorated when these eight…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData-Driven Disease Surveillance
