TY - JOUR T1 - Detecting within-host interactions from genotype combination prevalence data JF - bioRxiv DO - 10.1101/256586 SP - 256586 AU - Samuel Alizon AU - Carmen Lía Murall AU - Emma Saulnier AU - Mircea Sofonea Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/10/05/256586.abstract N2 - 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. ER -