I am currently a second year Applied Mathematics PhD student in the Bedford lab. My research is focused on developing methods to forecast the transmission and evolution of viruses by analyzing multiple data sources at various scales jointly. In particular, I am interested in how transmission, immune, and evolutionary dynamics occur simultaneously and how we can leverage the connections between these scales to better analyze outbreaks.
Marlin Figgins
PhD student
Papers
Frequency dynamics predict viral fitness, antigenic relationships and epidemic growth
Inferring variant-specific effective reproduction numbers from combined case and sequencing data
Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency
Spike deep mutational scanning helps predict success of SARS-CoV-2 clades
Positive selection underlies repeated knockout of ORF8 in SARS-CoV-2 evolution
Underdetected dispersal and extensive local transmission drove the 2022 mpox epidemic
Genomic surveillance of SARS-CoV-2 Omicron variants on a university campus