Inferring transmission trees of infectious disease outbreaks with multiple introductions and transmission routes: An epidemiological whodunnit

Like a detective in a murder mystery seeks the culprit, researchers studying infectious disease transmission aim to uncover who infected whom. Tracking links between infected individuals and identifying transmission events is essential for understanding many aspects of an outbreak. To solve this question, epidemiologists use a toolkit that includes two classes of methods: identifying transmission events through contact information and identifying them by tracing the evolution of the pathogen within the population.

In densely sampled outbreaks, where genetic sequences are available for almost all cases, methods have been developed that infer transmission events from genetic and epidemiological information. Yet the assumptions behind these methods can limit their use across different outbreak settings.

This thesis focuses on how infection of cases is represented in transmission models. One part examines the role of multiple introductions into a population. Another part studies the role of contact in transmission. Many current models assume a single introduction and treat all contact in the same way, which reduces their value for real outbreak analysis. To address these issues, this work expands the phybreak model to allow multiple introductions and to incorporate contact information, improving both practical use and accuracy.

Multiple introductions are common in infectious disease outbreaks, yet many inference methods assume only one index case. To resolve this, I extended the transmission inference model in the R package phybreak with a “history host”, an artificial source for the index cases. This allows the entire outbreak to be represented in one phylogenetic tree. This approach successfully identified repeated introductions of SARS CoV 2 into mink farms (Chapter 2) and showed that ongoing introductions were a key driver of the 2022 Mpox outbreak in Slovenia (Chapter 3). The method also performed well during real time analysis, where strongly supported introductions matched those later confirmed. Recognizing repeated introductions is important for effective public health action, since outside sources often sustain an outbreak.

Contact information is another essential source for understanding transmission, especially when used together with whole genome sequencing. In studies of Mycobacterium tuberculosis, cases are commonly grouped using SNP distance cutoffs, but these depend in part on the quality of contact tracing. In Chapter 4, phylodynamic methods were used to infer transmission events in the Netherlands. This supported the use of common SNP thresholds as a first step in identifying possible transmission pairs. Chapter 5 introduced another extension of phybreak that estimates the contribution of different types of contact to transmission. When applied to SARS CoV 2 in Dutch mink farms, the model showed that shared personnel accounted for most transmission whenever present, while other contact types were less influential.

The phybreak model is best suited for outbreaks with relatively few cases, nearly complete observation, and genetic sequences for most individuals. It captures transmission, observation, within host processes, and mutation, and is particularly useful when multiple introductions occur. Although challenges remain, the extensions presented here strengthen the use of phybreak for reconstruction of transmission events and support informed public health decisions.