PCI Ecology

Title

Detecting within-host interactions using genotype combination prevalence datause asterix (*) to get italics

Authors

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"

Year

2018

Picture

Abstract

<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>

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Keywords

modelling, virus, multiple infections, Approximate Bayesian Computing, within-host interacvtions

Methods that require specific expertise (optional)

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Thematic fields

Eco-immunology & Immunity, Epidemiology, Host-parasite interactions, Statistical ecology

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Submission date

2018-02-01 09:23:26

Recommender

Dustin Brisson

Reviewers

Samuel Díaz Muñoz, Erick Gagne

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