{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T23:54:03Z","timestamp":1778975643929,"version":"3.51.4"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T00:00:00Z","timestamp":1685664000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Despite its increasing role in the understanding of infectious disease transmission at the applied and theoretical levels, phylodynamics lacks a well-defined notion of ideal data and optimal sampling. We introduce a method to visualize and quantify the relative impact of pathogen genome sequence and sampling times\u2014two fundamental sources of data for phylodynamics under birth\u2013death-sampling models\u2014to understand how each drives phylodynamic inference. Applying our method to simulated data and real-world SARS-CoV-2 and H1N1 Influenza data, we use this insight to elucidate fundamental trade-offs and guidelines for phylodynamic analyses to draw the most from sequence data. Phylodynamics promises to be a staple of future responses to infectious disease threats globally. Continuing research into the inherent requirements and trade-offs of phylodynamic data and inference will help ensure phylodynamic tools are wielded in ever more targeted and efficient ways.<\/jats:p>","DOI":"10.1093\/molbev\/msad132","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T20:39:22Z","timestamp":1685738362000},"source":"Crossref","is-referenced-by-count":8,"title":["Decoding the Fundamental Drivers of Phylodynamic Inference"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8878-1758","authenticated-orcid":false,"given":"Leo A","family":"Featherstone","sequence":"first","affiliation":[{"name":"Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne , Melbourne, VIC , Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2863-0907","authenticated-orcid":false,"given":"Sebastian","family":"Duchene","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne , Melbourne, VIC , Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timothy G","family":"Vaughan","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich , Basel , Switzerland"},{"name":"Swiss Institute of Bioinformatics , Lausanne , Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,6,2]]},"reference":[{"key":"2023062211021438000_msad132-B1","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.coviro.2018.08.009","article-title":"Recent advances in computational 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