Arturo Torres Ortiz, Michelle Kendall, Nathaniel Storey, James Hatcher, Helen Dunn, Sunando Roy, Rachel Williams, Charlotte Williams, Richard A Goldstein, Xavier Didelot, Kathryn Harris, Judith Breuer, Louis Grandjean

Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is partially maintained among repeated serial samples from the same host, it can transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.

eLife. September 2023