Distinguishing new from persistent infections at the strain level using longitudinal genotyping data.

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Tác giả: Peter D Crompton, Sean C Murphy, Daniel E Neafsey, Amadou Niangaly, William A Nickols, Philipp Schwabl

Ngôn ngữ: eng

Ký hiệu phân loại: 003.209 Historical, geographic, persons treatment of forecasting as a discipline

Thông tin xuất bản: United States : bioRxiv : the preprint server for biology , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 732484

MOTIVATION: Longitudinal pathogen genotyping data from individual hosts can uncover strain-specific infection dynamics and their relationships to disease and intervention, especially in the malaria field. An important use case involves distinguishing newly incident from pre-existing (persistent) strains, but implementation faces statistical challenges relating to individual samples containing multiple strains, strains sharing alleles, and markers dropping out stochastically during the genotyping process. Current approaches to distinguish new versus persistent strains therefore rely primarily on simple rules that consider only the time since alleles were last observed. RESULTS: We developed DINEMITES ( AVAILABILITY AND IMPLEMENTATION: DINEMITES is freely available as an R package, along with documentation, tutorials, and example data, at https://github.com/WillNickols/dinemites .
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