Verification of model accuracy for estimating multiple infection prevalence on simulated data with incomplete sequencing success and false-negative and false-positive observations.

<p>(A) Number of windows with sequence data (x-axis) v. number of windows with multiple subgraphs (y-axis) for each simulated sample. Data from multiply infected samples is highlighted in red. Marginal distributions are shown at right and above. (B) Estimated posterior probability of multiple...

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Bibliographic Details
Main Author:	Michael A. Martin (14494499) (author)
Other Authors:	Andrea Brizzi (21162524) (author), Xiaoyue Xi (17238277) (author), Ronald Moses Galiwango (21162527) (author), Sikhulile Moyo (250724) (author), Deogratius Ssemwanga (444460) (author), Alexandra Blenkinsop (6253769) (author), Andrew D. Redd (6695216) (author), Lucie Abeler-Dörner (408693) (author), Christophe Fraser (7754) (author), Steven J. Reynolds (9176807) (author), Thomas C. Quinn (6695213) (author), Joseph Kagaayi (247946) (author), David Bonsall (5673770) (author), David Serwadda (150456) (author), Gertrude Nakigozi (247948) (author), Godfrey Kigozi (150435) (author), M. Kate Grabowski (2793961) (author), Oliver Ratmann (82388) (author)
Published:	2025
Subjects:	Medicine Microbiology Biotechnology Immunology Infectious Diseases Virology Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified throughput surveillance framework sequence data generated process called superinfection positive detection rates novel recombinant forms genetically distinct variants acquire secondary infections 3 – 3 identified multiple infections multiple infection provides multiple infection compared multiple hiv infections multiple infection ). short genome regions partial sequencing success novel bayesian deep div >< p 95 %– 5 hiv multiple infection 029 people living multiple infection people living simultaneous infection rccs ). participants living identifying people viremic hiv work introduces risk factors rccs participants quantifying prevalence plasma samples phylomi </ necessary conditions multiply infected level probabilities level prevalence insensitive bulk giving rise deep </ bias due based deep 45 %)
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Verification of model accuracy for estimating multiple infection prevalence on simulated data with incomplete sequencing success and false-negative and false-positive observations.

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