ArticleeLife2023
Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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.
Who cites it
8 citing papers in PubMed.
- Predicting the trend of SARS-CoV-2 mutation frequencies using historical data.Bioinformatics (Oxford, England) · 2025Article
- Impact of vaccination on Omicron's escape variants: Insights from fine-scale modelling of waning immunity in Hong Kong.Infectious Disease Modelling · 2025Article
- SARS-CoV-2 Alchemy: Understanding the dynamics of age, vaccination, and geography in the evolution of SARS-CoV-2 in India.PLoS neglected tropical diseases · 2025Article
- SARS-CoV-2 Evolution: Immune Dynamics, Omicron Specificity, and Predictive Modeling in Vaccinated Populations.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Exploring and retrieving sequence and metadata for species across the tree of life with NCBI Datasets.Scientific data · 2024Article
- COVID-19 outbreaks surveillance through text mining applied to electronic health records.BMC infectious diseases · 2024Article
- Therapeutic development targeting host heparan sulfate proteoglycan in SARS-CoV-2 infection.Frontiers in medicine · 2024Review
- Enhanced detection and molecular modeling of adaptive mutations in SARS-CoV-2 coding and non-coding regions using the c/µ test.Virus evolution · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID19 has aptly revealed that airborne viruses such as SARS-CoV-2 with the ability to rapidly mutate combined with high rates of transmission and fatality can cause a deadly worldwide pandemic in a matter of weeks (Plato et al., 2021). Apart from vaccines and post-infection treatment options, strategies for preparedness will be vital in responding to the current and future pandemics. Therefore, there is wide interest in approaches that allow predictions of increase in infections ('surges') before they occur. We describe here real-time genomic surveillance particularly based on mutation analysis, of viral proteins as a methodology for a priori determination of surge in number of infection cases. The full results are available for SARS-CoV-2 at http://pandemics.okstate.edu/covid19/, and are updated daily as new virus sequences become available. This approach is generic and will also be applicable to other pathogens.
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Registered trials
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.