Algorithmic Techniques for Inferring Transmission Networks from Noisy Sequencing Data

Funding agency: National Science Foundation, Division of Computing and Communication Foundations
Award #: CCF-1618347
Amount: $200,000
PI: Ion I. Mandoiu, Co-PI: Mukul Bansal
Period: 08/2016-07/2019


Many viruses encode their genome in RNA and exhibit high genomic diversity within their hosts. Advances in sequencing technologies have made it feasible to track viral transmissions and timely detect outbreaks on a global scale. The goal of this project is to develop a comprehensive set of predictive mathematical models and accurate computational methods for integrated analysis of the massive epidemiological and sequencing datasets generated by emerging molecular surveillance programs.