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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-1 evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5\u201350 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 that adding biomarkers improves reconstructions of real HIV-1 transmission histories. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009741","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T18:02:24Z","timestamp":1661536944000},"page":"e1009741","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":11,"title":["Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8840-8067","authenticated-orcid":true,"given":"Erik","family":"Lundgren","sequence":"first","affiliation":[]},{"given":"Ethan","family":"Romero-Severson","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9020-0521","authenticated-orcid":true,"given":"Jan","family":"Albert","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8160-2588","authenticated-orcid":true,"given":"Thomas","family":"Leitner","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,8,26]]},"reference":[{"key":"pcbi.1009741.ref001","author":"CDC) CfDCaP","year":"2021","journal-title":"HIV PREVENTION IN THE UNITED STATES: MOBILIZING TO END THE EPIDEMIC: Centers for Disease Control and Prevention (CDC)"},{"issue":"1","key":"pcbi.1009741.ref002","doi-asserted-by":"crossref","first-page":"e1006000","DOI":"10.1371\/journal.ppat.1006000","article-title":"Social and Genetic Networks of HIV-1 Transmission in New York City","volume":"13","author":"JO Wertheim","year":"2017","journal-title":"PLoS Pathog"},{"issue":"3","key":"pcbi.1009741.ref003","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1089\/aid.2006.0158","article-title":"Limitations to contact tracing and phylogenetic analysis in establishing HIV type 1 transmission networks in Cuba","volume":"23","author":"S Resik","year":"2007","journal-title":"AIDS Res Hum Retroviruses"},{"key":"pcbi.1009741.ref004","article-title":"Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks","author":"X Didelot","year":"2017","journal-title":"Mol Biol Evol"},{"issue":"1","key":"pcbi.1009741.ref005","doi-asserted-by":"crossref","first-page":"e1003457","DOI":"10.1371\/journal.pcbi.1003457","article-title":"Bayesian reconstruction of disease outbreaks by combining epidemiologic and genomic data","volume":"10","author":"T Jombart","year":"2014","journal-title":"PLoS Comput Biol"},{"issue":"5","key":"pcbi.1009741.ref006","doi-asserted-by":"crossref","first-page":"e1005495","DOI":"10.1371\/journal.pcbi.1005495","article-title":"Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks","volume":"13","author":"D Klinkenberg","year":"2017","journal-title":"PLoS Comput Biol"},{"issue":"9","key":"pcbi.1009741.ref007","doi-asserted-by":"crossref","first-page":"e1005130","DOI":"10.1371\/journal.pcbi.1005130","article-title":"SCOTTI: Efficient Reconstruction of Transmission within Outbreaks with the Structured Coalescent","volume":"12","author":"N De Maio","year":"2016","journal-title":"PLoS Comput Biol"},{"issue":"3","key":"pcbi.1009741.ref008","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1534\/genetics.113.154856","article-title":"Relating phylogenetic trees to transmission trees of infectious disease outbreaks","volume":"195","author":"RJ Ypma","year":"2013","journal-title":"Genetics"},{"key":"pcbi.1009741.ref009","article-title":"PHYLOSCANNER: Inferring Transmission from Within- and Between-Host Pathogen Genetic Diversity","author":"C Wymant","year":"2017","journal-title":"Mol Biol Evol"},{"issue":"1","key":"pcbi.1009741.ref010","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1093\/bioinformatics\/btx402","article-title":"QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data","volume":"34","author":"P Skums","year":"2018","journal-title":"Bioinformatics"},{"issue":"4","key":"pcbi.1009741.ref011","doi-asserted-by":"crossref","first-page":"e1006117","DOI":"10.1371\/journal.pcbi.1006117","article-title":"Bayesian reconstruction of transmission within outbreaks using genomic variants","volume":"14","author":"N De Maio","year":"2018","journal-title":"PLoS Comput Biol"},{"issue":"3","key":"pcbi.1009741.ref012","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1097\/COH.0000000000000536","article-title":"Phylogenetics in HIV transmission: taking within-host diversity into account","volume":"14","author":"T. 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