Pathogen genomic data is increasingly being used to investigate transmission dynamics in infectious disease outbreaks. Combining genomic data with epidemiological data should substantially increase our understanding of outbreaks, but this is highly challenging when the outbreak under study is only partially sampled, so that both genomic and epidemiological data are missing for intermediate links in the transmission chains. Here we present a new dynamic programming algorithm to perform this task efficiently. We implement this methodology into the well-established TransPhylo framework to reconstruct partially sampled outbreaks using a combination of genomic and epidemiological data. We use simulated datasets to show that including epidemiological data can improve the accuracy of the inferred transmission links compared to inference based on genomic data only. This also allows us to estimate parameters specific to the epidemiological data (such as transmission rates between particular groups) which would otherwise not be possible. We then apply these methods to two real-world examples. Firstly, we use genomic data from an outbreak of tuberculosis in Argentina, for which data was also available on the HIV status of sampled individuals, in order to investigate the role of HIV co-infection in the spread of this tuberculosis outbreak. Second, we use genomic and geographical data from the 2003 epidemic of avian influenza H7N7 in the Netherlands to reconstruct its spatial epidemiology. In both cases we show that incorporating epidemiological data into the genomic analysis allows us to investigate the role of epidemiological properties in the spread of infectious diseases.

The authors have declared no competing interest.

We acknowledge funding from the National Institute for Health Research (NIHR) Health Protection Research Unit in Genomics and Enabling Data (grant number NIHR200892).

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All data produced are available online at https://github.com/DrJCarson/TransPhylo2