Revival of the heterologous prime‐boost technique in <scp>COVID</scp>‐19: An outlook from the history of outbreaks
Bibliographic record
Abstract
BACKGROUND: The heterologous prime-boost vaccination technique is not novel as it has a history of deployment in previous outbreaks. AIM: Hence, this narrative review aims to provide critical insight for reviving the heterologous prime-boost immunization strategy for SARS-CoV-2 vaccination relative to a brief positive outlook on the mix-dose approach deployed in previous and existing outbreaks (ie, Ebola virus disease (EVD), malaria, tuberculosis, hepatitis B, HIV and influenza virus). METHODOLOGY AND MATERIALS: A Boolean search was carried out to find the literature in MEDLINE-PubMed, Clinicaltrials, and Cochrane Central Register of Controlled Trials databases up till December 22, 2021, using the specific keywords that include "SARS-CoV2", "COVID-19", "Ebola," "Malaria," "Tuberculosis," "Human Immunodeficiency Virus," "Hepatitis B," "Influenza," "Mix and match," "Heterologous prime-boost," with interposition of "OR" and "AND." Full text of all the related articles in English language with supplementary appendix was retrieved. In addition, the full text of relevant cross-references was also retrieved. RESULTS: Therefore, as generally evident by the primary outcomes, that is, safety, reactogenicity, and immunogenicity reported and updated by preclinical and clinical studies for addressing previous and existing outbreaks so far, including COVID-19, it is understood that heterologous prime-boost immunization has been proven successful for eliciting a more efficacious immune response as of yet in comparison to the traditional homologous prime-boost immunization regimen. DISCUSSION: Accordingly, with increasing cases of COVID-19, many countries such as Germany, Pakistan, Canada, Thailand, and the United Kingdom have started administering the heterologous vaccination as the technique aids to rationalize the usage of the available vaccines to aid in the global race to vaccinate majority to curb the spread of COVID-19 efficiently. However, the article emphasizes the need for more large controlled trials considering demographic details of vaccine recipients, which would play an essential role in clearing the mistrust about safety concerns to pace up the acceptance of the technique across the globe. CONCLUSION: Consequently, by combatting the back-to-back waves of COVID-19 and other challenging variants of concerns, including Omicron, the discussed approach can also help in addressing the expected evolution of priority outbreaks as coined by WHO, that is, "Disease X" in 2018 with competency, which according to WHO can turn into the "next pandemic" or the "next public health emergency," thus would eventually lead to eradicating the risk of "population crisis."
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".