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

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

Abstract readPreprint

In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

William A NickolsDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID 0000-0001-8214-9746

Philipp SchwablInfectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Amadou NiangalyMalaria Infection Biology and Immunity Section, Laboratory of Immunogenetics, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA.

Sean C MurphyDepartment of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.

Peter D CromptonMalaria Infection Biology and Immunity Section, Laboratory of Immunogenetics, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA.

Daniel E NeafseyInfectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Funding

Interdisciplinary training: Statistical Genetics/Genomics and Computational BiologyT32GM135117 · NIGMS · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Curtis Huttenhower, XIHONG LIN · 2020 to 2026

$3.1M

Exploring the roles of acquired immunity and functional constraint in sculpting malaria antigenic diversity in a longitudinal cohortR01AI141544 · NIAID · HARVARD SCHOOL OF PUBLIC HEALTH · PI NEAFSEY, DANIEL E · 2018 to 2022

$2.6M

NIAID NIH HHS R01 AI141544NIGMS NIH HHS T32 GM135117

6 · The paper itself

Abstract

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 (

Identifiers

PMID39975299

DOI10.1101/2025.02.06.636982

PMCPMC11839113

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