Proposing sequence genetic distances with a locally greedy algorithm.

<p>Our local greedy algorithm respects the infinite-sites assumption for mutations neighboring the newly proposed exposure time , along a lineage (represented by a blue line). We illustrate our algorithm using two scenarios in which both the newly proposed source and the infectee individual &l...

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Bibliographic Details
Main Author:	Hannah Waddel (21520023) (author)
Other Authors:	Katia Koelle (82611) (author), Max S. Y. Lau (9128033) (author)
Published:	2025
Subjects:	Biophysics Medicine Genetics Biotechnology Evolutionary Biology Infectious Diseases Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified major scalability issue infinite sites assumption existing phylodynamic approaches validated modeling framework phylodynamic modeling framework unobserved dynamics including temporal phylodynamic framework scalable bayesian inference genomic data collected effective outbreak responses accurate modeling framework method scales significantly mechanistically integrate epidemiological div >< p evolutionary dynamics including transmission dynamics consistent mouth disease outbreak unobserved transmission tree temporal dynamics evolutionary dynamics disease transmission transmission tree yet accurate highly scalable outbreak size modeling mutation evolutionary processes evolutionary process tree </ without relying various non underlying epidemiological sequence level results show results indicate readily deployable r package prior method previous work previous method powerful tool outbreaks continues nucleotide level new approach mostly rely modeled explicitly method ’ method provides large outbreaks joint spatio
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Proposing sequence genetic distances with a locally greedy algorithm.

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