Inferring Viral Transmission Pathways from Within-Host Variation

Genome sequencing can offer critical insight into pathogen spread in viral outbreaks, but existing transmission inference methods use simplistic evolutionary models and only incorporate a portion of available genetic data. Here, we develop a robust evolutionary model for transmission reconstruction that tracks the genetic composition of within-host viral populations over time and the lineages transmitted between hosts. We confirm that our model reliably describes within-host variant frequencies in a dataset of 134,682 SARS-CoV-2 deep-sequenced genomes from Massachusetts, USA. We then demonstrate that our reconstruction approach infers transmissions more accurately than two leading methods on synthetic data, as well as in a controlled outbreak of bovine respiratory syncytial virus and an epidemiologically-investigated SARS-CoV-2 outbreak in South Africa. Finally, we apply our transmission reconstruction tool to 5,692 outbreaks among the 134,682 Massachusetts genomes. Our methods and results demonstrate the utility of within-host variation for transmission inference of SARS-CoV-2 and other pathogens, and provide an adaptable mathematical framework for tracking within-host evolution. ### Competing Interest Statement P.C.S. is a co-founder and shareholder of Sherlock Biosciences and Delve Bio and is a non-executive board member and shareholder of Danaher Corporation. P.C.S. is an inventor on patents related to diagnostics and Bluetooth-based contact tracing tools and technologies filed with the USPTO and other intellectual property bodies. A patent application has been filed on inventions described in this manuscript. All other authors declare no competing interests. ### Funding Statement This work was supported in part by the Gordon and Betty Moore Foundation (#9125 and #9125.01 to P.C.S.), the National Institute of General Medical Sciences (T32GM007753 and T32GM144273 to B.A.P.), the National Institute of Allergy and Infectious Diseases (U19AI110818 and U01AI151812 to P.C.S.), the Centers for Disease Control and Prevention (75D30120C09605 to B.L.M.), the Rockefeller Foundation (2021HTH013 to B.L.M. and P.C.S.), the Howard Hughes Medical Institute (P.C.S.), Flu Lab (P.C.S.) as well as a cohort of generous donors through TED's Audacious Project, including the ELMA Foundation, MacKenzie Scott, the Skoll Foundation, and Open Philanthropy (P.C.S.). The empirical data set described was generated under support from the CDC COVID-19 baseline genomic surveillance contract sequencing (75D30121C10501 to the Clinical Research Sequencing Platform, LLC). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All sequencing data used in this study are publicly available through the NCBI SRA under BioProject PRJNA715749. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The outbreak reconstruction tool is available as an R package on GitHub, at github.com/broadinstitute/reconstructR. Code and data for all analyses and figures in this paper are available at github.com/broadinstitute/transmission-inference. All sequencing data used in this study are publicly available through the NCBI SRA under BioProject PRJNA715749.