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Detecting within-host interactions using genotype combination prevalence datause asterix (*) to get italics
Samuel Alizon, Carmen Lía Murall, Emma Saulnier, Mircea T SofoneaPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
<p>Parasite genetic diversity can provide information on disease transmission dynamics but most methods ignore the exact combinations of genotypes in infections. We introduce and validate a new method that combines explicit epidemiological modelling of coinfections and regression Approximate Bayesian Computing (ABC) to detect within-host interactions. Using genital infections by different types of Human Papillomaviruses (HPVs) as a test case, we show that, if sufficiently strong, within-host parasite interactions can be detected from epidemiological data and that this detection is robust even in the face of host heterogeneity in behaviour. These results suggest that the combination of mathematical modelling and sophisticated inference techniques is promising to extract additional epidemiological information from existing datasets.</p> should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://
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modelling, virus, multiple infections, Approximate Bayesian Computing, within-host interacvtions
Eco-immunology & Immunity, Epidemiology, Host-parasite interactions, Statistical ecology
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2018-02-01 09:23:26
Dustin Brisson
Samuel Díaz Muñoz, Erick Gagne