ArticleVaccines2023
A Simple Epidemiologic Model for Predicting Impaired Neutralization of New SARS-CoV-2 Variants.
Article in Vaccines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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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.
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Who cites it
4 citing papers in PubMed.
- The Crucial Role of Laboratory Medicine in Addressing Future Public Health Infectious Threats: Insights Gained from the COVID-19 Pandemic.Diagnostics (Basel, Switzerland) · 2025Article
- Fever duration, peak temperature and associated symptoms in Chinese adults with SARS-CoV-2 infection: a cross-sectional study.Frontiers in medicine · 2025Article
- A Multivariant Surrogate Neutralization Assay Identifies Variant-Specific Neutralizing Antibody Profiles in Primary SARS-CoV-2 Omicron Infection.Diagnostics (Basel, Switzerland) · 2023Article
- Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study is aimed at developing a simple epidemiologic model that could help predict the impaired neutralization of new SARS-CoV-2 variants. We explored the potential association between neutralization of recent and more prevalent SARS-CoV-2 sublineages belonging to the Omicron family (i.e., BA.4/5, BA.4.6, BA.2.75.2, BQ.1.1 and XBB.1) expressed as FFRNT50 (>50% suppression of fluorescent foci fluorescent focus reduction neutralization test) in recipients of four doses of monovalent mRNA-based coronavirus disease 2019 (COVID-19) vaccines, with epidemiologic variables like emergence date and number of spike protein mutations of these sublineages, cumulative worldwide COVID-19 cases and cumulative number of COVID-19 vaccine doses administered worldwide at the time of SARS-CoV-2 Omicron sublineage emergence. In the univariate analysis, the FFRNT50 value for the different SARS-CoV-2 Omicron sublineages was significantly associated with all such variables except with the number of spike protein mutations. Such associations were confirmed in the multivariate analysis, which enabled the construction of the equation: “−0.3917 × [Emergence (date)] + 1.403 × [COVID-19 cases (million)] − 121.8 × [COVID-19 Vaccine doses (billion)] + 18,250”, predicting the FFRNT50 value of the five SARS-CoV-2 Omicron sublineages with 0.996 accuracy (p = 0.013). We have shown in this work that a simple mathematical approach, encompassing a limited number of widely available epidemiologic variables, such as emergence date of new variants and number of COVID-19 cases and vaccinations, could help identifying the emergence and surge of future lineages with major propensity to impair humoral immunity.
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