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Detecting within-host interactions from genotype combination prevalence data

View ORCID ProfileSamuel Alizon, View ORCID ProfileCarmen Lía Murall, View ORCID ProfileEmma Saulnier, View ORCID ProfileMircea Sofonea
doi: https://doi.org/10.1101/256586
Samuel Alizon
1MIVEGEC, CNRS, IRD, Université de Montpellier, France
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Carmen Lía Murall
1MIVEGEC, CNRS, IRD, Université de Montpellier, France
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Emma Saulnier
1MIVEGEC, CNRS, IRD, Université de Montpellier, France
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Mircea Sofonea
1MIVEGEC, CNRS, IRD, Université de Montpellier, France
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  • ORCID record for Mircea Sofonea
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Abstract

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.

Footnotes

  • ↵* samuel.alizon{at}cnrs.fr

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Posted October 05, 2018.
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Detecting within-host interactions from genotype combination prevalence data
Samuel Alizon, Carmen Lía Murall, Emma Saulnier, Mircea Sofonea
bioRxiv 256586; doi: https://doi.org/10.1101/256586
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Detecting within-host interactions from genotype combination prevalence data
Samuel Alizon, Carmen Lía Murall, Emma Saulnier, Mircea Sofonea
bioRxiv 256586; doi: https://doi.org/10.1101/256586

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