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Recent work has attempted to use whole-genome sequence data from pathogens toreconstruct the transmission trees linking infectors and infectees inoutbreaks. However, transmission trees from one outbreak do not generalize tofuture outbreaks. Reconstruction of transmission trees is most useful to publichealth if it leads to generalizable scientific insights about diseasetransmission. In a survival analysis framework, estimation of transmissionparameters is based on sums or averages over the possible transmission trees. Aphylogeny can increase the precision of these estimates by providing partialinformation about who infected whom. The leaves of the phylogeny representsampled pathogens, which have known hosts. The interior nodes represent commonancestors of sampled pathogens, which have unknown hosts. Starting fromassumptions about disease biology and epidemiologic study design, we prove thatthere is a one-to-one correspondence between the possible assignments ofinterior node hosts and the transmission trees simultaneously consistent withthe phylogeny and the epidemiologic data on person, place, and time. We developalgorithms to enumerate these transmission trees and show these can be used tocalculate likelihoods that incorporate both epidemiologic data and a phylogeny.A simulation study confirms that this leads to more efficient estimates ofhazard ratios for infectiousness and baseline hazards of infectious contact,and we use these methods to analyze data from a foot-and-mouth disease virusoutbreak in the United Kingdom in 2001. These results demonstrate theimportance of data on individuals who escape infection, which is oftenoverlooked. The combination of survival analysis and algorithms linkingphylogenies to transmission trees is a rigorous but flexible statisticalfoundation for molecular infectious disease epidemiology.

Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees