RT Journal Article
SR Electronic
T1 Detecting within-host interactions from genotype combination prevalence data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 256586
DO 10.1101/256586
A1 Samuel Alizon
A1 Carmen Lía Murall
A1 Emma Saulnier
A1 Mircea Sofonea
YR 2018
UL http://biorxiv.org/content/early/2018/10/05/256586.abstract
AB 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.