Andrew is a broadly trained mathematical biologist currently developing machine learning approaches that allow us to forecast the risk of pathogen spillover. Click here for more information on Andrew's current work.
Mathematical models of disease often focus on single strain pathogen dynamics. By extending these approaches to consider multiple-strains it becomes possible to address a number of interesting problems. In particular, between-strain competition can make estimating the outcomes of vaccination programs challenging. Nathan is currently working with the Nuismer lab to develop multi-strain viral models to address the following questions. First, how can reversion to virulence or loss of efficacy be prevented in transmissible vaccines? Second, how does evolution post-release influence the ability of transmissible vaccines to invade target populations? Third, how is pathogen genetic diversity influenced by fluctuating vector population sizes? Nathan is also interested in utilizing remote sensing data and predictive models to control zoonotic diseases in wildlife populations before they can threaten human populations.
Anna is an ecologist interested in understanding the role that host and virus diversity play in the dynamics of viral infection. She uses a forecasting framework to better understand where and when wildlife viruses pose a risk to human health.
PhD Student (BCB)
Tanner's research focuses on understanding how heterogeneity in host populations influences the effectiveness of transmissible vaccines and how GIS data can be used to forecast the emergence of infectious disease.
MS Student (BCB)
Courtney's research focuses on developing mathematical models optimizing the delivery of vaccines to fluctuating wildlife populations.
Beth Tuschhoff (BS)
Bob Week (PhD)
Benji Oswald (PhD)
Ben Ridenhour (Postdoc)
Virginie Poullain (PhD)
Francois Blanquart (Undergraduate/PhD)
Florence Debarre (Postdoc)
Anahi Espindola (Postdoc)
Ailene MacPherson (MS)