Abstract
Genomic tools, including phylogenetic trees derived from sequence data, are increasingly used to understand outbreaks of infectious diseases. One challenge is to link phylogenetic trees to patterns of transmission. Particularly in bacteria that cause chronic infections, this inference is affected by variable infectious periods and infectivity over time. It is known that non-exponential infectious periods can have substantial effects on pathogens’ transmission dynamics. Here we ask how this non-Markovian nature of an outbreak process affects the branching trees describing that process, with particular focus on tree shapes. We simulate Crump-Mode-Jagers branching processes and compare different patterns of infectivity over time. We find that memory (non-Markovian-ness) in the process can have a pronounced effect on the shapes of the outbreak’s branching pattern. However, memory also has a pronounced effect on the sizes of the trees, even when the duration of the simulation is fixed. When the sizes of the trees are constrained to a constant value, memory in our processes has little direct effect on tree shapes, but can bias inference of the birth rate from trees. We compare simulated branching trees to phylogenetic trees from an outbreak of tuberculosis in Canada, and discuss the relevance of memory to this dataset.
Highlights
Genomic tools, including phylogenetic trees derived from sequence data, are increasingly used to understand outbreaks of infectious diseases
An ongoing challenge in epidemiology is to make the best use of genomic data, usually with the help of inference and analysis of phylogenetic trees that carry information on parameters including the basic reproduction number (R0)[9,10]
Under good conditions, a timed phylogenetic tree can be seen as an approximate representation of the true branching tree, though it does not include the information of who infected whom in a direct way
Summary
Genomic tools, including phylogenetic trees derived from sequence data, are increasingly used to understand outbreaks of infectious diseases. An ongoing challenge in epidemiology is to make the best use of genomic data, usually with the help of inference and analysis of phylogenetic trees that carry information on parameters including the basic reproduction number (R0)[9,10]. The link between pairwise genetic diversity and who infected whom has been widely studied and discussed[6,7,8,11,12,13,14,15,16,17] These assumptions may break down for various reasons, but the study of branching trees remains a central tool for modelling phylogenetic trees. The epidemiological coalescent can account for non-exponential durations of infectiousness and variable infectivity[15,19,21] but in models with many compartments, the necessary inference becomes challenging due to large numbers of latent variables[15]
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