Abstracts (first author)


Using model selection to deduce the influence of pathogen growth on the mortality trajectory of the host

Author(s): Priest NK


It is well established that individual mortality trajectories are influenced by both innate and external factors. However, we currently have no way of estimating the relative importance of factors that contribute to complex mortality trajectories, such as those which occur during epidemics. Because infection generates heterogeneity between individuals it is difficult to solve even the most elementary epidemiological questions, including whether individuals survive epidemics because they cleared the pathogen or because they never got infected. Here we use an information-theoretic approach to address this problem. First, we constructed a set of demographic models that described plausible influences of infection and cohort heterogeneity on host mortality. Next, we conducted a temperature-specific demographic study of the age-specific mortality rate of roughly 10,000 Drosophila melanogaster topically infected with Metarhizium robertsii, an entomopathogenic fungus. Finally, we estimated the fit of each mathematical model to the mortality data. We found that the all of the best fit and most efficient models in the model set described individual mortality trajectory as being driven by a rapid increase and subsequent decrease in fungal load, which provides substantial evidence that insects do indeed clear fungal infections. We also identified a significant cohort heterogeneity parameter, the estimate of which was large enough to shift the observed mortality peaks earlier and weaker than the underlying infection. This work illustrates the value of model selection in epidemiology and provides a novel method for testing the roles of infection and inflammation in healthy ageing.



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XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon


Computational Biology & Population Genomics Group