COS 102-1
Network-based vaccination improves prospects for disease control in wild chimpanzee

Thursday, August 8, 2013: 1:30 PM
L100A, Minneapolis Convention Center
Julie L. Rushmore, Odum School of Ecology, University of Georgia, Athens, GA
Damien Caillaud, The University of Texas at Austin
Richard J. Hall, Odum School of Ecology, University of Georgia, Athens, GA
Rebecca M. Stumpf, University of Illinois at Urbana-Champaign
Lauren Ancel Meyers, Section of Integrative Biology, The University of Texas at Austin, Austin, TX
Sonia Altizer, Odum School of Ecology, University of Georgia, Athens, GA

Many endangered wildlife populations are vulnerable to infectious diseases for which vaccines exist; yet, endangered species are rarely vaccinated, in part owing to the challenges in immunizing large portions of a population.  Focusing intervention efforts on individuals with the highest contact rates could minimize the number of animals requiring vaccination. Great apes demonstrate tremendous variation in social contacts and have experienced major population declines from directly-transmitted pathogens. We present here the first study using detailed behavioral association data and epidemiological network models to design and evaluate disease intervention strategies for a wild primate population. Outbreaks were simulated on monthly contact networks parameterized with association data from a wild chimpanzee community to ask how final outbreak size depends on network position of the index case, outbreak timing, and pathogen infectiousness. By determining traits associated with individuals most likely to spark large epidemics, we identified “risk groups” that could be targeted for pathogen control.


Simulations of three vaccination strategies revealed that compared to random vaccinations, the number of animals requiring immunization to curb outbreaks could be reduced by up to 35% if chimpanzees were vaccinated based on greatest network centrality and by up to 18% if efforts focused on individuals from easily identifiable risk groups. Overall, our work demonstrates that parameterizing epidemiological models with fine-scale association data can help optimize pathogen control efforts for endangered wildlife.