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arxiv: 2606.07129 · v2 · pith:OMVWMR2Onew · submitted 2026-06-05 · 📊 stat.AP

Collaborative estimation and evaluation of SARS-CoV-2 variant nowcasting in the United States

Isaac MacArthur , Thomas Robacker , Bren Case , Spencer J. Fox , Dylan H. Morris , Evan L. Ray , Benjamin Rogers , Becky Sweger
show 22 more authors
Natalie M. Linton John Huddleston Andrew Magee Zachary Susswein Jover Lee Trevor Bedford Marlin D. Figgins Ehsan Suez Rajath Prabhakar Tomas Leon Brent Siegel Mugdha Thakur Christopher M. Hoover Rahil Ryder Jesse Elder Michael Kupperman Ruian Ke Emma Goldberg Sebastian Funk Maryclare Griffin Nicholas G. Reich Kaitlyn E. Johnson
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Pith reviewed 2026-06-27 20:29 UTC · model grok-4.3

classification 📊 stat.AP
keywords SARS-CoV-2variant nowcastingcollaborative forecastingmodel evaluationbaseline modelsequencing volumestate-level estimatespublic health surveillance
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The pith

A simple national baseline model for SARS-CoV-2 variant frequencies performs as well as or better than most individual models submitted to a new US nowcast hub.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a collaborative United States SARS-CoV-2 Variant Nowcast Hub that collects state-level estimates of variant relative abundances and evaluates submitted models against a baseline that pools sequences nationally. Using data from October 2024 through June 2025, it finds the baseline competitive overall while most individual models perform similarly or slightly worse, with performance varying more where sequencing volumes are low and single-location submissions sometimes showing an edge. This matters because accurate short-term variant tracking supports planning for transmission shifts, immunity changes, and adjustments to vaccines or treatments. A sympathetic reader would care because the work supplies a concrete test bed for whether centralized pooling or specialized models better serve public health surveillance needs.

Core claim

The central claim is that in the Hub's first respiratory virus season, the baseline model pooling sequences across the U.S. performs well overall, with most individual models performing similarly or slightly worse; locations with lower sequencing volumes show greater variability in model performance; and models submitted for a single location outperform those submitted for all locations, potentially due to greater timeliness and magnitude of local data.

What carries the argument

The United States SARS-CoV-2 Variant Nowcast Hub together with its scoring procedures for comparing state-level variant abundance estimates from multiple models against observed sequence data.

If this is right

  • The baseline national pooling approach supplies a strong reference that most submitted models do not clearly beat.
  • Model performance becomes more variable in states that have lower sequencing volumes.
  • Models focused on one location can outperform all-location models because they incorporate more timely or larger local data sets.
  • Performance differences may depend on the phase of variant emergence, requiring further targeted checks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • For other pathogens with patchy surveillance, national pooling could serve as a default that is hard to beat without extra local effort.
  • Low-sequencing states might gain most from added data collection rather than from more complex models.
  • The Hub structure could be extended to test whether the same baseline advantage appears for influenza or other respiratory viruses.

Load-bearing premise

The models and evaluation period from the first season provide a representative test of relative performance across different locations and data volumes.

What would settle it

In a later season with new variant dynamics, most individual models consistently and substantially outperform the national baseline across many states with varying sequencing volumes.

Figures

Figures reproduced from arXiv: 2606.07129 by Andrew Magee, Becky Sweger, Benjamin Rogers, Bren Case, Brent Siegel, Christopher M. Hoover, Dylan H. Morris, Ehsan Suez, Emma Goldberg, Evan L. Ray, Isaac MacArthur, Jesse Elder, John Huddleston, Jover Lee, Kaitlyn E. Johnson, Marlin D. Figgins, Maryclare Griffin, Michael Kupperman, Mugdha Thakur, Natalie M. Linton, Nicholas G. Reich, Rahil Ryder, Rajath Prabhakar, Ruian Ke, Sebastian Funk, Spencer J. Fox, Thomas Robacker, Tomas Leon, Trevor Bedford, Zachary Susswein.

Figure 1
Figure 1. Figure 1: Illustration of clade assignment using different combinations of dates when sequences are available and [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Weekly SARS-CoV-2 variant dynamics in the U.S. from September 2nd, 2024 to June 14th, 2025 using [PITH_FULL_IMAGE:figures/full_fig_p025_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: SARS-CoV-2 sequence counts and clade frequencies for an example nowcast date of February 19th, 2025 [PITH_FULL_IMAGE:figures/full_fig_p026_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Example model outputs alongside the observed clade frequencies as of February 19th, 2025. (A) Nowcasted [PITH_FULL_IMAGE:figures/full_fig_p027_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example model predicted observed frequencies with prediction intervals as of February 19th, 2025. Model [PITH_FULL_IMAGE:figures/full_fig_p028_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Score comparison over all evaluated nowcast dates, horizons, and jurisdictions (referred to as overall). [PITH_FULL_IMAGE:figures/full_fig_p029_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Score comparison of the absolute and relative Brier and energy scores across nowcast dates and horizons for [PITH_FULL_IMAGE:figures/full_fig_p030_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Score comparison by nowcast date stratified by the U.S. excluding California (left) and California (right). [PITH_FULL_IMAGE:figures/full_fig_p031_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visual and quantitative performance comparison of probabilistic models during emergence of 25A in [PITH_FULL_IMAGE:figures/full_fig_p032_9.png] view at source ↗
read the original abstract

The ability to estimate and predict pathogen variant dynamics can inform public health responses, including planning for increased transmission or severity, shifts in population immunity, or changes to vaccine or therapeutic effectiveness. The COVID-19 pandemic demonstrated the importance of monitoring SARS-CoV-2 variant evolution through viral genome sequencing, enabling predictive models to estimate variant frequencies in the recent past, present, and short-term future. Collaborative forecasting Hubs provided a valuable way to centralize predictive modeling of epidemiological indicators such as cases, hospitalizations, and deaths during the pandemic; however, none existed for variant dynamics. Here, we discuss the creation of the United States SARS-CoV-2 Variant Nowcast Hub, designed to solicit estimates of the relative abundance of a specified set of SARS-CoV-2 variants at the U.S. state level. We discuss the design decisions and challenges in building the Hub and its scoring procedures. Using submissions from the Hub's first respiratory virus season (nowcast dates October 9th, 2024 to June 4th, 2025), we evaluate five individual models and a baseline model. We found that the baseline model, which pools sequences across the U.S., performs well overall, with most individual models performing similarly or slightly worse. Locations with lower sequencing volumes exhibited greater variability in model performance. Models submitted for a single location outperformed those submitted for all locations, potentially due to greater timeliness and magnitude of local data. Much remains to be investigated regarding relative model performance across different phases of variant emergence, and we conclude by proposing future directions within and beyond this Hub.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript describes the creation of the United States SARS-CoV-2 Variant Nowcast Hub for soliciting state-level estimates of SARS-CoV-2 variant relative abundances. It covers design decisions, challenges, and scoring procedures. Using submissions from the first respiratory virus season (nowcast dates October 9, 2024 to June 4, 2025), the authors evaluate five individual models plus a national-pooling baseline, reporting that the baseline performs well overall while most individual models perform similarly or slightly worse, with greater variability in low-sequencing-volume locations and better performance for location-specific submissions.

Significance. If the evaluation holds, the work establishes a new collaborative infrastructure for variant nowcasting that fills a gap left by prior COVID-19 forecasting hubs. The empirical comparison against external sequence data supplies initial benchmarks and highlights practical effects of sequencing volume and submission scope. Strengths include the open Hub framework and the explicit acknowledgment that further investigation is needed across variant emergence phases; these elements support reproducibility and incremental progress in applied statistical surveillance.

major comments (1)
  1. [Evaluation / Results] The central performance claims rest on an empirical evaluation whose scoring rules, data exclusion criteria, error handling, and any statistical tests are not described in the abstract and are only alluded to in the provided summary. Without these details (e.g., the precise loss function, weighting by sequencing volume, or handling of zero-count locations), the statement that the baseline “performs well overall” cannot be independently verified and is load-bearing for the paper’s main result.
minor comments (2)
  1. [Abstract] Abstract: the sentence on scoring procedures could be expanded by one clause naming the primary metric (e.g., weighted absolute error or log-score) to give readers immediate context.
  2. [Methods] The manuscript would benefit from a concise table listing the five individual models, their submission scopes (single-location vs. all-location), and key methodological differences.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review. We address the single major comment below and have revised the manuscript accordingly to improve transparency of the evaluation.

read point-by-point responses

  1. Referee: [Evaluation / Results] The central performance claims rest on an empirical evaluation whose scoring rules, data exclusion criteria, error handling, and any statistical tests are not described in the abstract and are only alluded to in the provided summary. Without these details (e.g., the precise loss function, weighting by sequencing volume, or handling of zero-count locations), the statement that the baseline “performs well overall” cannot be independently verified and is load-bearing for the paper’s main result.

    Authors: We agree that the abstract does not contain these methodological details, which is standard due to length limits, and that the main result would benefit from greater accessibility. The full manuscript describes the scoring rules, data exclusion criteria, error handling, and evaluation approach (including weighting by sequencing volume and treatment of zero-count locations) in the dedicated Scoring and Evaluation sections. No formal statistical tests are used; comparisons are descriptive. To address the concern directly, we have revised the abstract to include a concise statement of the evaluation metric and weighting scheme, and we have added an explicit summary paragraph in the Results section that restates the key aspects of the scoring procedure. These changes allow independent verification of the claim that the baseline performs well overall without altering the underlying analysis. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical evaluation only

full rationale

The paper reports an empirical comparison of nowcasting model submissions against external SARS-CoV-2 sequence data over October 2024–June 2025. No equations, derivations, or first-principles predictions appear; the baseline (national pooling) is a simple, non-fitted reference evaluated directly on held-out observations. No self-citation chains, ansatzes, or fitted-input-as-prediction steps are load-bearing. The central claims rest on observable performance differences by location and model type, making the analysis self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract supplies no explicit free parameters, invented entities, or detailed axioms; the work rests on standard domain assumptions about the representativeness of genomic surveillance data.

axioms (1)
  • domain assumption Genomic sequence data accurately reflect underlying variant frequencies at state level
    Implicit throughout the description of nowcasting and model evaluation against observed sequences.

pith-pipeline@v0.9.1-grok · 5939 in / 1229 out tokens · 21737 ms · 2026-06-27T20:29:46.740230+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

64 extracted references · 42 canonical work pages

  1. [1]

    Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency

    Eslam Abousamra, Marlin Figgins, and Trevor Bedford. “Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency”. In:PLOS Computational Biology20.9 (2024), e1012443.doi:10.1371/ journal.pcbi.1012443

    2024

  2. [2]

    Nextclade: clade assignment, mutation calling and quality control for viral genomes

    Ivan Aksamentov et al. “Nextclade: clade assignment, mutation calling and quality control for viral genomes”. In:Journal of Open Source Software6.67 (2021), p. 3773.doi:10.21105/joss.03773.url:https://doi. org/10.21105/joss.03773

    work page doi:10.21105/joss.03773.url:https://doi 2021

  3. [3]

    Nextstrain automates real-time phylodynamic analysis of open data for endemic and emerging pathogens

    Kirsty R Andrews et al. “Nextstrain automates real-time phylodynamic analysis of open data for endemic and emerging pathogens”. In:bioRxiv2026.03.23.713807 (2026). Preprint.doi:10.64898/2026.03.23.713807

    work page doi:10.64898/2026.03.23.713807 2026

  4. [4]

    Molecular Evolution and Epidemiology of Norovirus GII.4 Viruses in the United States

    Leslie Barclay et al. “Molecular Evolution and Epidemiology of Norovirus GII.4 Viruses in the United States”. In:The Journal of Infectious Diseases232.4 (Oct. 2025), pp. 933–942.issn: 1537-6613.doi:10.1093/infdis/ jiaf100.url:https://doi.org/10.1093/infdis/jiaf100

    work page doi:10.1093/infdis/ 2025

  5. [5]

    “GenBank”

    Dennis A Benson et al. “GenBank”. In:Nucleic acids research41.D1 (2012), pp. D36–D42

    2012

  6. [6]

    Binaural room impulse responses recorded with KEMAR in a small meeting room,

    Nikos I. Bosse et al.scoringutils: Utilities for Scoring and Assessing Predictions. 2020.doi:10.5281/zenodo. 4618017.url:https://cran.r-project.org/package=scoringutils

    work page doi:10.5281/zenodo 2020

  7. [7]

    Evaluating epidemic forecasts in an interval format

    Johannes Bracher et al. “Evaluating epidemic forecasts in an interval format”. In:PLOS Computational Biology17.2 (2021), e1008618.doi:10.1371/journal.pcbi.1008618

    work page doi:10.1371/journal.pcbi.1008618 2021

  8. [8]

    Novel (d)PCR assays for influenza A(H5Nx) viruses clade 2.3.4.4b surveillance

    Gerhard Buttinger et al. “Novel (d)PCR assays for influenza A(H5Nx) viruses clade 2.3.4.4b surveillance”. In: Eurosurveillance30.33 (2025). Received: 12 Mar 2025; Accepted: 26 Jun 2025, pii=2500183.doi:10.2807/ 1560- 7917.ES.2025.30.33.2500183.url:https://doi.org/10.2807/1560- 7917.ES.2025.30.33. 2500183

    work page doi:10.2807/1560- 2025

  9. [9]

    Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data

    Finlay Campbell et al. “Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data”. In:PLOS Computational Biology15.3 (2019), e1006930.doi:10.1371/journal.pcbi. 1006930

    work page doi:10.1371/journal.pcbi 2019

  10. [10]

    GitHub repository

    CDC FluSight Team.FluSight Forecast Hub: Repository for Forecasts of Influenza Hospitalizations. GitHub repository. Accessed: 2026-01-12. 2023.url:https://github.com/cdcepi/FluSight-forecast-hub

    2026

  11. [11]

    https://ndc.services.cdc.gov/wp- content/uploads/MMWR Week overview.pdf

    Centers for Disease Control and Prevention.MMWR Week Fact Sheet. https://ndc.services.cdc.gov/wp- content/uploads/MMWR Week overview.pdf

  12. [12]

    cdc.gov/covid/php/variants/variants-and-genomic-surveillance.html

    Centers for Disease Control and Prevention.SARS-CoV-2 Variants and Genomic Surveillance.https://www. cdc.gov/covid/php/variants/variants-and-genomic-surveillance.html. Accessed: 2026-01-11. 2024

    2026

  13. [13]

    Accessed: 2026-01-11

    Chaoran Chen et al.CoV-Spectrum: Analysis of Globally Shared SARS-CoV-2 Data to Identify and Char- acterize New Variants.https://cov- spectrum.org/explore/United%20Kingdom/AllSamples/Past6M. Accessed: 2026-01-11. 2022. 20

    2026

  14. [14]

    Global landscape of SARS-CoV-2 genomic surveillance and data sharing

    Zugen Chen et al. “Global landscape of SARS-CoV-2 genomic surveillance and data sharing”. In:Nature Genetics54 (2022). Published online 28 March 2022, pp. 499–507.doi:10.1038/s41588-022-01033-y

    work page doi:10.1038/s41588-022-01033-y 2022

  15. [15]

    Coordinating collaborative infectious disease modeling projects with the hubverse

    Consortium of Infectious Disease Modeling Hubs et al. “Coordinating collaborative infectious disease modeling projects with the hubverse”. In:medRxiv(2025). Preprint.doi:10 . 1101 / 2025 . 10 . 03 . 25337284.url: https://www.medrxiv.org/content/10.1101/2025.10.03.25337284v1

    work page doi:10.1101/2025.10.03.25337284v1 2025

  16. [16]

    COVID-19 Genomics UK (COG-UK) Consortium.COG-UK Consortium.https : / / www . sanger . ac . uk / collaboration/covid- 19- genomics- uk- cog- uk- consortium/. Accessed: 2026-01-11. Wellcome Sanger Institute, 2020

    2026

  17. [17]

    Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

    Estee Y Cramer et al. “Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States”. In:Proceedings of the National Academy of Sciences119.15 (2022), e2113561119.doi: 10.1073/pnas.2113561119

    work page doi:10.1073/pnas.2113561119 2022

  18. [18]

    Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic

    Alexander Crits-Christoph et al. “Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic”. In:Cell187.19 (2024), 5468–5482.e11.doi:10.1016/j.cell.2024.08.010

    work page doi:10.1016/j.cell.2024.08.010 2024

  19. [19]

    Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England

    Nicholas G. Davies et al. “Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England”. In:Science372.6538 (2021), eabg3055.doi:10.1126/science.abg3055. eprint:https://www.science. org/doi/pdf/10.1126/science.abg3055.url:https://www.science.org/doi/abs/10.1126/science. abg3055

    work page doi:10.1126/science.abg3055 2021

  20. [20]

    CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting

    Yinan Feng et al. “CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting”. In:Virus Evolution10.1 (2024), veae086.doi:10.1093/ve/veae086.url:https://doi.org/10.1093/ve/ veae086

    work page doi:10.1093/ve/veae086.url:https://doi.org/10.1093/ve/ 2024

  21. [21]

    Figgins and Trevor Bedford.Frequency dynamics predict viral fitness, antigenic relationships and epidemic growth

    Marlin D. Figgins and Trevor Bedford.Frequency dynamics predict viral fitness, antigenic relationships and epidemic growth. medRxiv preprint. 2024.doi:10.1101/2024.12.02.24318334.url:https://doi.org/ 10.1101/2024.12.02.24318334

    work page doi:10.1101/2024.12.02.24318334.url:https://doi.org/ 2024

  22. [23]

    Methods combining genomic and epidemiological data in the reconstruction of trans- mission trees: a systematic review

    Fabr` ıcia Giardina et al. “Methods combining genomic and epidemiological data in the reconstruction of trans- mission trees: a systematic review”. In:Pathogens11.2 (2022), p. 252.doi:10.3390/pathogens11020252

    work page doi:10.3390/pathogens11020252 2022

  23. [24]

    Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E

    Tilmann Gneiting and Adrian E. Raftery. “Strictly Proper Scoring Rules, Prediction, and Estimation”. In: Journal of the American Statistical Association102.477 (2007), pp. 359–378.doi:10.1198/016214506000001437

    work page doi:10.1198/016214506000001437 2007

  24. [25]

    Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds

    Tilmann Gneiting et al. “Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds”. In:Test17 (2008), pp. 211–235

    2008

  25. [26]

    Nextstrain: real-time tracking of pathogen evolution

    James Hadfield et al. “Nextstrain: real-time tracking of pathogen evolution”. In:Bioinformatics34.23 (2018), pp. 4121–4123.doi:10.1093/bioinformatics/bty407.url:https://academic.oup.com/bioinformatics/ article/34/23/4121/5001388

    work page doi:10.1093/bioinformatics/bty407.url:https://academic.oup.com/bioinformatics/ 2018

  26. [27]

    org / sars - cov - 2 / forecasts

    James Hadfield et al.Nextstrain: SARS-CoV-2 Forecasts.https : / / nextstrain . org / sars - cov - 2 / forecasts. Accessed: 2026-01-16. 2024

    2026

  27. [28]

    Preprint.https : / / doi

    James A Hay et al.Evaluation of the epidemiological outlook of the influenza A/H3N2 clade K in England during the 2025-26 season. Preprint.https : / / doi . org / 10 . 5281 / zenodo . 17704679. Nov. 2025.doi: 10.5281/zenodo.17704679

    work page doi:10.5281/zenodo.17704679 2025

  28. [29]

    The origins and molecular evolution of SARS-CoV-2 lineage B.1.1.7 in the UK

    Verity Hill et al. “The origins and molecular evolution of SARS-CoV-2 lineage B.1.1.7 in the UK”. In:Virus Evolution8.2 (2022), veac080.doi:10.1093/ve/veac080

    work page doi:10.1093/ve/veac080 2022

  29. [30]

    Integrating genotypes and phenotypes improves long-term forecasts of seasonal in- fluenza A/H3N2 evolution

    John Huddleston et al. “Integrating genotypes and phenotypes improves long-term forecasts of seasonal in- fluenza A/H3N2 evolution”. In:eLife9 (2020), e60067.doi:10.7554/eLife.60067. 21

    work page doi:10.7554/elife.60067 2020

  30. [31]

    https://github.com/hubverse-org

    The Consortium of Infectious Disease Modeling Hubs.The hubverse: open tools for collaborative modeling. https://github.com/hubverse-org. GitHub release v5.0.0, 17 Jan 2025, Accessed: 2025-06-13. 2025

    2025

  31. [32]

    Real-Time Projections of SARS-CoV-2 B.1.1.7 Variant in a University Setting, Texas, USA

    Kaitlyn E Johnson et al. “Real-Time Projections of SARS-CoV-2 B.1.1.7 Variant in a University Setting, Texas, USA”. In:Emerging Infectious Diseases27.12 (Dec. 2021), pp. 3188–3190.doi:10.3201/eid2712. 210652.url:https://doi.org/10.3201/eid2712.210652

    work page doi:10.3201/eid2712 2021

  32. [33]

    Evaluating probabilistic forecasts with scoringRules

    Alexander Jordan, Fabian Kr¨ uger, and Sebastian Lerch. “Evaluating probabilistic forecasts with scoringRules”. In:Journal of Statistical Software90 (2019), pp. 1–37

    2019

  33. [34]

    SARS-CoV-2 vaccine strain selection: Guidance from influenza

    Florian Krammer and Nadine Rouphael. “SARS-CoV-2 vaccine strain selection: Guidance from influenza”. In:Open Forum Infectious Diseases10.6 (2023), ofad239.doi:10.1093/ofid/ofad239

    work page doi:10.1093/ofid/ofad239 2023

  34. [35]

    Reproducible and later vaccine strain selection can improve vaccine match to A/H3N2 seasonal influenza viruses

    Sojung Lee, C´ ecile Viboud, and Trevor Bedford. “Reproducible and later vaccine strain selection can improve vaccine match to A/H3N2 seasonal influenza viruses”. In:npj Vaccines10 (2025), p. 23.doi:10.1038/s41541- 025-01292-w

    work page doi:10.1038/s41541- 2025

  35. [36]

    Challenges of COVID-19 Case Forecasting in the US, 2020–2021

    Velma K Lopez et al. “Challenges of COVID-19 Case Forecasting in the US, 2020–2021”. In:PLOS Compu- tational Biology20.5 (2024), e1011200.doi:10.1371/journal.pcbi.1011200

    work page doi:10.1371/journal.pcbi.1011200 2020

  36. [37]

    Dengue serotypes and epidemic dynamics in Brazil: a spatiotemporal perspective

    Camila Lorenz, Philippe Lemey, and Simon Dellicour. “Dengue serotypes and epidemic dynamics in Brazil: a spatiotemporal perspective”. In:Travel Medicine and Infectious Disease68 (2025), p. 102948.doi:10.1016/ j.tmaid.2025.102948.url:https://doi.org/10.1016/j.tmaid.2025.102948

    work page doi:10.1016/j.tmaid.2025.102948 2025

  37. [38]

    Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations

    Sarabeth M. Mathis et al. “Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations”. In:Nature Communications15 (2024), p. 6289.doi:10.1038/s41467-024-50601-9

    work page doi:10.1038/s41467-024-50601-9 2021

  38. [39]

    Evidence of latency reshapes our understanding of Ebola virus reservoir dynamics

    John T McCrone et al. “Evidence of latency reshapes our understanding of Ebola virus reservoir dynamics”. In:bioRxiv(Oct. 2025). Preprint.doi:10.1101/2025.10.17.683141.url:https://www.biorxiv.org/ content/10.1101/2025.10.17.683141v1

    work page doi:10.1101/2025.10.17.683141.url:https://www.biorxiv.org/ 2025

  39. [40]

    Drivers of COVID-19 variant wave dynamics: inferring oncoming wave size using global data with genomics

    S. Molan et al. “Drivers of COVID-19 variant wave dynamics: inferring oncoming wave size using global data with genomics”. In:medRxiv(2025).doi:10.1101/2025.09.16.25335896. eprint:https://www.medrxiv. org/content/early/2025/09/18/2025.09.16.25335896.full.pdf.url:https://www.medrxiv.org/ content/early/2025/09/18/2025.09.16.25335896

    work page doi:10.1101/2025.09.16.25335896 2025

  40. [41]

    Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2

    Sang Woo Park et al. “Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2”. In:Proceedings of the National Academy of Sciences120.22 (2023), e2221887120.doi:10.1073/pnas.2221887120. eprint:https://www.pnas.org/doi/pdf/10.1073/ pnas.2221887120.url:https://www.pnas.org/doi/abs/...

    work page doi:10.1073/pnas.2221887120 2023

  41. [42]

    Open-source database for human viral pathogen genomic data

    Pathoplexus.Pathoplexus. Open-source database for human viral pathogen genomic data. 2024.url:https: //pathoplexus.org

    2024

  42. [43]

    Genomic Surveillance for SARS-CoV-2 Variants Circulating in the United States, De- cember 2020–May 2021

    Prabasaj Paul et al. “Genomic Surveillance for SARS-CoV-2 Variants Circulating in the United States, De- cember 2020–May 2021”. In:MMWR. Morbidity and Mortality Weekly Report70.23 (2021), pp. 846–850.doi: 10.15585/mmwr.mm7023a3

    work page doi:10.15585/mmwr.mm7023a3 2020

  43. [44]

    Detection of Hemagglutinin H5 Influenza A Virus RNA and Model of Potential Inputs in an Urban California Sewershed

    Abigail P. Paulos et al. “Detection of Hemagglutinin H5 Influenza A Virus RNA and Model of Potential Inputs in an Urban California Sewershed”. In:Environmental Science & Technology59.31 (2025), pp. 16168–16179. doi:10.1021/acs.est.4c14792

    work page doi:10.1021/acs.est.4c14792 2025

  44. [45]

    Considerable escape of SARS-CoV-2 Omicron to antibody neutralization

    Delphine Planas et al. “Considerable escape of SARS-CoV-2 Omicron to antibody neutralization”. In:Nature 602.7898 (2022), pp. 671–675

    2022

  45. [46]

    Generation of SARS-CoV-2 escape mutations by monoclonal antibody ther- apy

    Manon Ragonnet-Cronin et al. “Generation of SARS-CoV-2 escape mutations by monoclonal antibody ther- apy”. In:Nature Communications14.1 (2023), p. 3334.doi:10.1038/s41467-023-37826-w.url:https: //doi.org/10.1038/s41467-023-37826-w. 22

    work page doi:10.1038/s41467-023-37826-w.url:https: 2023

  46. [47]

    A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology

    Andrew Rambaut et al. “A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology”. In:Nature Microbiology(2020).doi:10.1038/s41564-020-0770-5

    work page doi:10.1038/s41564-020-0770-5 2020

  47. [48]

    Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

    Evan L. Ray et al. “Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States”. In:International Journal of Forecasting39 (2023), pp. 1366–1383.doi: 10.1016/j.ijforecast.2022.06.005

    work page doi:10.1016/j.ijforecast.2022.06.005 2023

  48. [49]

    Nicholas G Reich et al.Collaborative hubs: making the most of predictive epidemic modeling. 2022

    2022

  49. [50]

    A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States

    Nicholas G. Reich et al. “A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States”. In:Proceedings of the National Academy of Sciences116.8 (2019), pp. 3146–3154.doi: 10.1073/pnas.1812594116

    work page doi:10.1073/pnas.1812594116 2019

  50. [51]

    https://github.com/reichlab/variant- nowcast-hub

    Reich Lab at UMass-Amherst.US SARS-CoV-2 Variant Nowcast Hub. https://github.com/reichlab/variant- nowcast-hub. Accessed: 2024-12-27. 2024

    2024

  51. [52]

    Importation of Alpha and Delta variants during the SARS-CoV-2 epidemic in Switzerland: Phylogenetic analysis and intervention scenarios

    Martina L. Reichmuth, Emma B. Hodcroft, and Christian L. Althaus. “Importation of Alpha and Delta variants during the SARS-CoV-2 epidemic in Switzerland: Phylogenetic analysis and intervention scenarios”. In:PLoS Pathogens19.8 (Aug. 2023), e1011553.doi:10.1371/journal.ppat.1011553

    work page doi:10.1371/journal.ppat.1011553 2023

  52. [53]

    Thomas Robacker et al.Evaluating predictions of multi-class population proportions: A variant nowcasting case study.work in progress

  53. [54]

    https://next.nextstrain.org/nextclade/sars- cov-2

    Cornelius Roemer and Richard Neher.SARS-CoV-2 phylogeny. https://next.nextstrain.org/nextclade/sars- cov-2. 2024

    2024

  54. [55]

    GISAID: Global initiative on sharing all influenza data – from vision to reality

    Yuelong Shu and John McCauley. “GISAID: Global initiative on sharing all influenza data – from vision to reality”. In:Eurosurveillance22.13 (2017).doi:10.2807/1560-7917.ES.2017.22.13.30494

    work page doi:10.2807/1560-7917.es.2017.22.13.30494 2017

  55. [56]

    Comparative analysis of Mpox clades: epidemiology, transmission dynamics, and detection strategies

    Shriyansh Srivastava et al. “Comparative analysis of Mpox clades: epidemiology, transmission dynamics, and detection strategies”. In:BMC Infectious Diseases25 (Oct. 2025), p. 1290.doi:10 . 1186 / s12879 - 025 - 11784-8.url:https://pmc.ncbi.nlm.nih.gov/articles/PMC12516921/

    2025

  56. [57]

    Leveraging global genomic sequencing data to estimate local variant dynamics

    Zachary Susswein et al. “Leveraging global genomic sequencing data to estimate local variant dynamics”. In: medRxiv(2023).doi:10.1101/2023.01.02.23284123. eprint:https://www.medrxiv.org/content/early/ 2023/03/20/2023.01.02.23284123.full.pdf.url:https://www.medrxiv.org/content/early/2023/ 03/20/2023.01.02.23284123

    work page doi:10.1101/2023.01.02.23284123 2023

  57. [58]

    Python Package Index

    Becky Sweger et al.cladetime: Python interface for accessing Nextstrain SARS-CoV-2 sequence and clade data. Python Package Index. Available at https://pypi.org/project/cladetime/. 2024.url:https://github. com/reichlab/cladetime

    2024

  58. [59]

    Founder effects arising from gathering dynamics systematically bias emerging pathogen surveillance

    Bradford P Taylor and William P Hanage. “Founder effects arising from gathering dynamics systematically bias emerging pathogen surveillance”. In:eLife Sciences Publications, Ltd(Mar. 2025).doi:10.7554/elife. 104201.1.url:http://dx.doi.org/10.7554/eLife.104201.1

    work page doi:10.7554/elife 2025

  59. [60]

    Emergence of SARS-CoV-2 Omicron lineages BA.4 and BA.5 in South Africa

    Houriiyah Tegally et al. “Emergence of SARS-CoV-2 Omicron lineages BA.4 and BA.5 in South Africa”. In: Nature Medicine28.9 (Sept. 2022), pp. 1785–1790.doi:10.1038/s41591-022-01911-2

    work page doi:10.1038/s41591-022-01911-2 2022

  60. [61]

    The evolution and biology of SARS-CoV-2 variants

    Amalio Telenti, Emma B Hodcroft, and David L Robertson. “The evolution and biology of SARS-CoV-2 variants”. In:Cold Spring Harbor perspectives in medicine12.5 (2022), a041390

    2022

  61. [62]

    Technical Briefing

    UK Health Security Agency.SARS-CoV-2 variants of concern and variants under investigation in Eng- land: Technical briefing 38. Technical Briefing. Published: 11 March 2022. Mar. 2022.url:https : / / assets . publishing . service . gov . uk / media / 622b4a20e90e070ed8233a05 / Technical - Briefing - 38 - 11March2022.pdf

    2022

  62. [63]

    Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England

    Erik Volz et al. “Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England”. In:Nature593 (2021), pp. 266–269.doi:10.1038/s41586-021-03470-x.url:https://www.nature.com/articles/s41586-021- 03470-x. 23

    work page doi:10.1038/s41586-021-03470-x.url:https://www.nature.com/articles/s41586-021- 2021

  63. [64]

    Implementation of California COVIDNet – a multi-sector collaboration for statewide SARS-CoV-2 genomic surveillance

    Debra A. Wadford et al. “Implementation of California COVIDNet – a multi-sector collaboration for statewide SARS-CoV-2 genomic surveillance”. In:Frontiers in Public Health11 (2023), p. 1249614.doi:10 . 3389 / fpubh.2023.1249614

    arXiv 2023

  64. [65]

    Collaborative nowcasting of COVID-19 hospitalization incidences in Germany

    Daniel Wolffram et al. “Collaborative nowcasting of COVID-19 hospitalization incidences in Germany”. In: PLOS Computational Biology19.8 (2023), e1011394.doi:10.1371/journal.pcbi.1011394. 24 Figure 2: Weekly SARS-CoV-2 variant dynamics in the U.S. from September 2nd, 2024 to June 14th, 2025 using the final set of sequences available 90 days after the last ...

    work page doi:10.1371/journal.pcbi.1011394 2023