Evidence map›Paper›PMID 40705028›Full record

ArticleEmerging infectious diseases2025

Estimated COVID-19 Periodicity and Correlation with SARS-CoV-2 Spike Protein S1 Antigenic Diversity, United States.

Erica Billig Rose, Clinton R Paden, Peter W Cook, Kevin C Ma, Amber Winn, Juan Castro, Lakshmi Panagiotakopoulos, Benjamin J Silk

Abstract read
In one paragraph

Article in Emerging infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Respiratory Virus Activity - United States, July 1, 2024-June 30, 2025.MMWR. Morbidity and mortality weekly report · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
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

8 authors.

Erica Billig Rose
Clinton R Paden
Peter W Cook
Kevin C Ma
Amber Winn
Juan Castro
Lakshmi Panagiotakopoulos
Benjamin J Silk

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emergence of antigenically diverse SARS-CoV-2 variants may be correlated with temporal circulation patterns. We analyzed positive SARS-CoV-2 tests in the United States reported to a national, laboratory-based surveillance network and unique amino acid sequences of the S1 region of the spike protein reported to national genomic surveillance during October 2020-September 2024. We estimated SARS-CoV-2 dominant periodicities using a discrete Fourier transform, described S1 variation using the Simpson diversity index (SDI), and estimated Spearman cross-correlation coefficients between percentage change in SDI and percentage positivity. SARS-CoV-2 activity consistently peaked during July-September and December-February, and dominant periodicities were at weeks 52.2 and 26.1. Percentage positivity and percentage change in SDI were negatively correlated (ρ = -0.30; p<0.001). SARS-CoV-2 peaks occurred in late summer and winter, a pattern likely related to rapid SARS-CoV-2 evolution and cyclical diversity. Monitoring associations between percentage positivity and SDI can help forecast expected surges and optimize prevention and preparedness.

Indexed as

Antigenic VariationCOVID-19SARS-CoV-2Spike Glycoprotein, CoronavirusHumansSeasonsUnited StatesSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2antigenic variationcoronaviruscoronavirus diseaseCOVID-19respiratory infectionsSARSSARS-CoV-2seasonssevere acute respiratory syndrome coronavirus 2spike proteinUnited Statesviruseszoonoses

Identifiers

PMID40705028
PMCPMC12309744

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.