Detecting within-host interactions from genotype combination prevalence data

Samuel Alizon (1, 2) , Carmen Lia Murall (3) , Emma Saulnier (3) , Mircea T Sofonea (1)

1 MIVEGEC-ETE - Evolution Théorique et Expérimentale
2 CIRB - Centre interdisciplinaire de recherche en biologie
3 MIVEGEC - Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle

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).

Mots clés: Multiple infections; MOI; Superspreaders; ABC; Inference; Competition

Domaines: Santé publique et épidémiologie; Modélisation et simulation; Interactions entre organismes; Maladies infectieuses

Samuel Alizon, Carmen Lia Murall, Emma Saulnier, Mircea T Sofonea. Detecting within-host interactions from genotype combination prevalence data. Epidemics, 2019, 29, pp.100349. ⟨10.1016/j.epidem.2019.100349⟩. ⟨hal-02387765⟩