Detecting within-host interactions from genotype combination prevalence data.
ABC
Competition
Inference
MOI
Multiple infections
Superspreaders
Journal
Epidemics
ISSN: 1878-0067
Titre abrégé: Epidemics
Pays: Netherlands
ID NLM: 101484711
Informations de publication
Date de publication:
12 2019
12 2019
Historique:
received:
11
10
2018
revised:
29
05
2019
accepted:
03
06
2019
pubmed:
2
7
2019
medline:
25
7
2020
entrez:
2
7
2019
Statut:
ppublish
Résumé
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).
Identifiants
pubmed: 31257014
pii: S1755-4365(18)30156-7
doi: 10.1016/j.epidem.2019.100349
pmc: PMC6899502
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
100349Informations de copyright
Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.
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