Iowa State University is leading a global collaboration to develop a scalable, early-warning system for animal and zoonotic disease outbreaks. The project, titled "Viral Databases, Algorithms, and Statistical Models for Rapid Pathogen-Agnostic Metagenomic Surveillance and Phylodynamics," combines next-generation sequencing with advanced statistical models to detect pathogens without prior assumptions about what diseases might be present.
The innovative approach represents a significant advancement in disease surveillance, enabling rapid identification of emerging threats before they become widespread outbreaks. By using metagenomic sequencing—which analyzes all genetic material in a sample rather than looking for specific pathogens—the system can detect both known and novel disease agents.
Led by principal investigator Michael Zeller, the interdisciplinary team brings together expertise from multiple institutions and disciplines. Iowa State contributors include Oliver Eulenstein, Karin Dorman (professor of statistics), and Sriram Vijendran. Additional team members include Tavis Anderson and Alexey Markin from USDA Agricultural Research Service, and Jackie Mahar, Rebecca Grimwood, and Matthew Neave from Australia's Commonwealth Scientific and Industrial Research Organisation (CSIRO).
The collaboration leverages statistical modeling expertise from the Department of Statistics to develop algorithms that can rapidly analyze complex genomic data and identify patterns indicating disease outbreaks. This work has significant implications for animal health, food security, and public health worldwide.
