SCOTTI: Reconstructing Transmission within Outbreaks combining genetic and epidemiological data
Exploiting pathogen genomes to reconstruct transmission represents a powerful tool in the fight against infectious disease. However, important complexities of real data are often ignored, in particular within-host pathogen evolution and non-sampled patients. I will present a new approach to transmission inference called SCOTTI (Structured COalescent Transmission Tree Inference). SCOTTI model each host as a distinct pathogen population, and transmissions between hosts as migration events. Our implementation enables efficient inference of host-to-host transmission while accommodating within-host evolution and non-sampled hosts. I will illustrate the features of this approach by investigating transmission from genetic and epidemiological data in a Foot and Mouth Disease Virus (FMDV) veterinary outbreak in England and a Klebsiella pneumoniae outbreak in a Nepali neonatal unit. I will also summarize recent progress in modelling migration and bacterial recombination within phylogenetics.
Nicola de Maio (Oxford University): cmmid@seminar