Research Interests


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I am a quantitative disease ecologist with broad interests in disease dynamics, wildlife health, and conservation.  My work combines host ecology or community ecology to understand infection dynamics. My main research interests include:

  • Disease dynamic consequences of co-infecting pathogens

  • Emerging infectious disease

  • Land use change and vector borne disease

  • Network and spatial models of diseases transmission

  • Wildlife health and population dynamics


About Me


Great news - as of August, I have started an assistant professor position in the University of Warwick’s School of Life Sciences and the Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research.

Please see the University of Warwick’s website for up-to date information.


Ongoing Projects


Land-use change and mosquito-borne infections

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Mosquito-borne infections remain a leading cause of morbidity and mortality in sub-Saharan Africa.  We are working with South African National Parks to study how human developments influence seasonal mosquito population dynamics and mosquito-borne disease risk.

 

 

 

Collaborators: Maarten Schrama, Danny Govender

 

Co-Infections in African buffalo

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Interactions between co-infecting pathogens are ubiquitous in wildlife populations. Recent research has shown that co-infecting pathogens can be the most important predictors of subsequent infections and that many interactions are mediated by the host immune response.  In this research, we explore the consequences of co-infection at many scales.   We use tools from both disease ecology and eco-immunology to study bovine tuberculosis and brucellosis in a free ranging population of African buffalo in Kruger National Park.  

Project PIs and Ph.D. advisors: Anna Jolles and Vanessa Ezenwa

Collaborators: Rampal Etienne, Roy Bengis, Brianna Beechler            

                                                            

U.S. Livestock Movement and Disease

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The application of network analysis to livestock shipments broadens our understanding of shipment patterns beyond pairwise interactions to the network as a whole.  Such a quantitative description of livestock shipments in the U.S. can identify trade communities, describe temporal shipment patterns, and inform the design of disease surveillance and control strategies.  This collaborative project uses data-driven models to study livestock shipments and how they influence disease spread. 

Project PIs and post-doc advisors: Colleen Webb, Uno Wennergren, Michael Tildesley

Collaborators: Michael BuhnerkempeAngela D. LuisLindsay M. Beck-Johnson, Daniel A. Grear, Ryan S. Miller, Katie Portacci