Combining biomarker and virus phylogenetic models improves epidemiological source identification

To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5-50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling, and we evaluate real HIV-1 transmission clusters. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies. AUTHOR SUMMARYWhile phylogenetic methods have been successfully used to infer transmission patterns on many scales, substantial uncertainty about the corresponding transmission history has remained. Here, we introduce a formal inference framework for the simultaneous use of HIV biomarkers and HIV sequences derived from persons who may have infected each other. We created a flexible system that can use up to five biomarkers to estimate the time of infection of each person, with the appropriate uncertainty given by an empirical probability distribution. These time-of-infection distributions were jointly modelled with the HIV phylogenetic tree estimation to produce possible transmission histories. We show that adding biomarkers substantially limits the possible transmission histories. This makes it possible to identify transmission risks, to assess confidence in source attributions, and to make efficient resource allocations to prevent further transmissions in local epidemics.