Evidence map›Paper›PMID 41959754›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Understanding patterns of variant emergence and spread in an ongoing epidemic.

Anjalika Nande, Michael Z Levy, Alison L Hill

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. 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

3 authors.

Anjalika NandeInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0003-1726-6633
Michael Z LevyDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-4661-3764
Alison L HillInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.

Funding

An immune system for the city: a new paradigm for control of urban disease vectorsR01AI146129 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI LEVY, MICHAEL Z, PAZ-SOLDAN, VALERIE ANDREA · 2019 to 2023
$3.3M
Quantification and prediction of treatment efficacy for HIV cure strategiesDP5OD019851 · OD · JOHNS HOPKINS UNIVERSITY · PI HILL, ALISON LYNN · 2014 to 2018
$2.1M
NIAID NIH HHS R01 AI146129NIH HHS DP5 OD019851
6 · The paper itself

Abstract

The COVID-19 pandemic saw successive emergence and global spread of novel viral variants, exhibiting enhanced transmissibility or evasion of immunity. While the genotypic and phenotypic basis of SARS-CoV-2 variants have been extensively characterized, the evolutionary factors governing their patterns of emergence are less well understood. In this study we systematically investigated how the invasion dynamics of viral variants depend on variant phenotype (increased transmissibility or immune evasion), source (local evolution vs importation), the timing of introduction, the distribution of population susceptibility, and the contact network structure. Using a stochastic multi-strain epidemic model, we find that strains with only a transmission advantage are more likely to emerge earlier in the epidemic, and rapidly and predictably dominate the viral population. In contrast, immune-escape variants tend to linger at low prevalence for extended time periods after emergence, avoiding detection, until a critical amount of immunity has built up in the population and they begin to rapidly outcompete existing strains. We find that two common features of realistic human contact networks-heterogeneity in contacts (overdispersion) and clustering-lead to more punctuated evolutionary dynamics. This work provides insight into past dynamics of SARS-CoV-2 variants and can help define planning scenarios for future epidemic modeling efforts.

Identifiers

PMID41959754
PMCPMC13060446

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.