Detecting Within-host Interactions from Genotype Combination Prevalence Data

Overview

Journal Epidemics

Publisher Elsevier

Specialties Infectious Diseases
Public Health

Date 2019 Jul 2

PMID 31257014

Citations 5

Authors

Samuel Alizon

Carmen Lia Murall

Emma Saulnier

Mircea T Sofonea

Affiliations

Soon will be listed here.

Abstract

Parasite genetic diversity can provide information on disease transmission dynamics but most mathematical and statistical frameworks 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 a susceptible-infected-susceptible (SIS) model, we show that, if sufficiently strong, within-host parasite interactions can be detected from epidemiological data. We also show that, in this simple setting, this detection is robust even in the face of some level of host heterogeneity in behaviour. These simulations results offer promising applications to analyse large datasets of multiple infection prevalence data, such as those collected for genital infections by Human Papillomaviruses (HPVs).

Citing Articles

Reconstructing multi-strain pathogen interactions from cross-sectional survey data via statistical network inference.

Man I, Beninca E, Kretzschmar M, Bogaards J J R Soc Interface. 2023; 20(205):20220912.

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Reconstructing contact network structure and cross-immunity patterns from multiple infection histories.

Selinger C, Alizon S PLoS Comput Biol. 2021; 17(9):e1009375.

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Potential Effects of Human Papillomavirus Type Substitution, Superinfection Exclusion and Latency on the Efficacy of the Current L1 Prophylactic Vaccines.

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Evolutionary ecology of Lyme Borrelia.

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Coinfections by noninteracting pathogens are not independent and require new tests of interaction.

Hamelin F, Allen L, Bokil V, Gross L, Hilker F, Jeger M PLoS Biol. 2019; 17(12):e3000551.

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