SARS-CoV-2 infection after COVID-19 vaccinations among vaccinated individuals, prevention rate of COVID-19 vaccination: A systematic review and meta-analysis
Bibliographic record
Abstract
Background The global concern regarding protection against the COVID-19 variants through pre-existing antibodies from vaccination or previous infection is evident. Reports from around the world indicate that a considerable number of healthcare professionals/individuals experience re-infection despite being vaccinated. Moreover, several studies have highlighted cases of symptomatic SARS-CoV-2 re-infection, specifically among individuals who have been vaccinated. Understanding the factors that contribute to these re-infections is crucial for implementing effective public health measures and enhancing vaccination strategies. Method A comprehensive search was conducted between January 1, 2021, and February 14, 2024, using various reputable sources such as PubMed, Google scholar, Medline, EMBASE, CINAHL, and others. The search aimed to retrieve relevant research on topics related to "world nations" and phrases like "COVID-19 vaccination breakthrough infection," "SARS re-infection after COVID-19 vaccination," "COVID-19 vaccine complication," "post COVID-19 vaccination symptoms," and specific nation names. The data obtained from the databases underwent extraction and quality assessment using the Newcastle-Ottawa Scale (NOS) and the Preferred Reporting Items for Systematic Review and Meta-Analyses. Data analysis was performed using STATA 17 MP software, and measures such as the I 2 test statistic and Egger's test were used to assess heterogeneity and publication bias. The findings were presented using forest plots, displaying the odds ratio (OR) and 95 % confidence interval (CI). Result This review and meta-analysis comprised a total of 15 articles, or a total sample size of 342,598. The pooled prevalence of SARS-CoV-2 after vaccination of COVID-19 was 9 % (95CI 7%–11 %) of population globally. This implied that reduced the overall attack rate of COVID-19 by 91 % after vaccination. The highest pooled estimated of SARS-CoV-2 infection after COVID -19 Vaccinations was seen among developing nations, 20 % (95 % CI: 5%–36 %).The pooled odds ratio showed that a significant association was found between SARS-CoV-2 infection after COVID-19 vaccination and older age (OR = 2.04; 95%CI: 1.10–2.98) and comorbidity (OR = 3.25; 95%CI: 1.04–5.47). Conclusion It is important for policymakers to prioritize continuous monitoring and surveillance of SARS-CoV-2 infection rates among vaccinated individuals globally, as there is a significant estimate of the combined prevalence of post-COVID-19 vaccine SARS-CoV-2 infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".