How does a vaccine get approved and how are side effects monitored?

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1. 1

   Global disparities in regulatory approval of prophylactic vaccines, 2005-2024.

   Journal of global health (Wang et al.)Published Jul 10, 2026Checked Oct 11, 2026

   “We conducted an exploratory multivariable linear regression analysis to identify factors potentially associated with approval delays.ResultsOf the 416 prophylactic vaccines, 67.8% received approval in only one CTA, and just 12.8% reached more than five CTAs between 2005 and 2024. By 2024, the European region had the highest total number of approvals (n = 2410), while Africa had the fewest (n = 56). Median global approval delay for prophylactic vaccines was 213 days (interquartile range (IQR) = 93.0, 768.8), ranging from 161.0 days (IQR = 84.0, 646.0) in Europe to 705.5 days (IQR = 183.3, 2220.3) in the Western Pacific region. Our multivariable models showed that a lower income level may be associated with longer delay. Other factors, such as the type of vaccine and regulatory designation, were also identified as potential correlates.ConclusionsDisparities in vaccine approval volume and timing persist across regions. Targeted policy actions are needed to strengthen regulatory systems in LMICs and reduce structural barriers to equitable vaccine access globally.”

2. 2

   Real-world safety data of human papillomavirus vaccines after marketing in Shandong, China, August 2017-October 2025.

   Human vaccines & immunotherapeutics (Xie et al.)Published Sep 25, 2026Checked Oct 11, 2026

   “The safety data from post-marketing can enhance public confidence in vaccines. We collected adverse events following immunization (AEFI) through the Chinese National AEFI Information Management System (CNAEFIs) between August 2017 and October 2025. Vaccination data from the same period were obtained via the Shandong Provincial Immunization Program Information Systems (SDIPIs). A Univariate Poisson regression model was used to explore risk factors associated with HPV vaccine AEFI. Disproportionality analysis was performed to detect abnormal signals related to the HPV vaccine. From August 2017 to October 2025 in Shandong, CNAEFIs received 1056 AEFI cases, with an overall reporting AEFI rate of 7.99 per 100,000 doses. 1008 cases (95.46%) were mild vaccine-related reactions, and 15 cases (1.42%) were rare adverse reactions. Myalgia, induration with a diameter greater than 5 cm, immune thrombocytopenia, and mild reactions such as fatigue, drowsiness, dizziness, and headache were detected as abnormal signals. The reporting AEFI rate of the HPV vaccine in Shandong was low, and the majority of AEFIs were mild cases. The safety profile in post-marketing was good.”

3. 3

   Vaccine (Wikipedia)

   WikipediaPublished Oct 10, 2026Checked Oct 11, 2026

   “A vaccine is a biological preparation that provides active acquired immunity to a particular infectious or malignant disease. The safety and effectiveness of vaccines has been widely studied and verified. A vaccine typically contains an agent that resembles a disease-causing microorganism and is often made from weakened or killed forms of the microbe, its toxins, or one of its surface proteins. The agent stimulates the immune system to recognize the agent as a threat, destroy it, and recognize further and destroy any of the microorganisms associated with that agent that it may encounter in the future. Vaccines can be prophylactic (to prevent or alleviate the effects of a future infection by a natural or "wild" pathogen), or therapeutic (to fight a disease that has already occurred, such as cancer). Some vaccines offer full sterilizing immunity, in which infection is prevented. The administration of vaccines is called vaccination.”

4. 4

   Vaccine adverse event (Wikipedia)

   WikipediaPublished Oct 6, 2026Checked Oct 11, 2026

   “A vaccine adverse event (VAE), sometimes referred to as a vaccine injury, is an adverse event believed to have been caused by vaccination. The World Health Organization (WHO) refers to Adverse Events Following Immunization (AEFI). AEFIs can be related to the vaccine itself (product or quality defects), to the vaccination process (administration error or stress related reactions) or can occur independently from vaccination (coincidental). Most vaccine adverse events are mild. Serious injuries and deaths caused by vaccines are very rare, and the idea that severe events are common has been classed as a "common misconception about immunization" by the WHO. Some claimed vaccine injuries are not, in fact, caused by vaccines; for example, there is a subculture of advocates who attribute their children's autism to vaccine injury, despite the fact that vaccines do not cause autism. Claims of vaccine injuries appeared in litigation in the United States in the latter part of the 20th century. Some families have won substantial awards from sympathetic juries, even though many public health officials have said that the claims of injuries are unfounded.”

5. 5

   A Bayesian approach to sequential analysis in post-licensure vaccine safety surveillance.

   Pharmaceutical statistics (Li et al.)Published Dec 22, 2019Checked Oct 11, 2026

   “With rapid development of computing technology, Bayesian statistics have increasingly gained more attention in various areas of public health. However, the full potential of Bayesian sequential methods applied to vaccine safety surveillance has not yet been realized, despite acknowledged practical benefits and philosophical advantages of Bayesian statistics. In this paper, we describe how sequential analysis can be performed in a Bayesian paradigm in the field of vaccine safety. We compared the performance of the frequentist sequential method, specifically, Maximized Sequential Probability Ratio Test (MaxSPRT), and a Bayesian sequential method using simulations and a real world vaccine safety example. The performance is evaluated using three metrics: false positive rate, false negative rate, and average earliest time to signal. Depending on the background rate of adverse events, the Bayesian sequential method could significantly improve the false negative rate and decrease the earliest time to signal. We consider the proposed Bayesian sequential approach to be a promising alternative for vaccine safety surveillance.”

6. 6

   Machine learning-assisted mRNA vaccine pharmacovigilance: a systematic review of multi-source real-world data.

   BMC medical informatics and decision making (He et al.)Published Sep 9, 2026Checked Oct 11, 2026

   “Searches were performed in PubMed, Embase, and Web of Science. Two reviewers independently screened records. Given substantial heterogeneity across ML tasks (signal detection, text extraction, risk prediction, prognosis stratification), algorithms, data sources, and metrics, we performed narrative synthesis. Risk of bias was assessed using adapted QUADAS-2.ResultsWe identified 43 studies. For adverse-event prediction, tree-based models reported AUCs of 0.85-0.87, though estimates derive from heterogeneous settings. NLP reduced redundant signals by 17% in vaccine reporting systems. For myocarditis, ML models reached AUCs up to 0.899 in cardiovascular cohorts, but direct mRNA vaccine applications remain limited and retrospective. Emerging platforms (self-amplifying and tumor mRNA vaccines) lack post-marketing data, rendering ML applications largely conceptual.ConclusionML-assisted pharmacovigilance enables a shift from passive to active, intelligent monitoring. Despite challenges in data quality, model interpretability, and regulatory approval, intelligent pharmacovigilance systems will become essential infrastructure for safeguarding public health.”

7. 7

   Global patterns and trends in spontaneous reporting systems: VigiBase, FAERS, VAERS, and JADER.

   Frontiers in drug safety and regulation (Panajotovikj & Mestres)Published Sep 4, 2026Checked Oct 11, 2026

   “Following deduplication, approximately 11.1 million reports were uniquely shared between VigiBase and FAERS and close to 1.3 million between VigiBase and VAERS.ResultsWe described and compared report features such as drug and adverse event reporting patterns, reporter types, geographic distribution, demographics, and medical outcomes. Most reports have been received in recent years across the four databases with up to 70% of the reports accumulated in the last 7-14 years, depending on the SRS. Notable patterns appeared in reporter types (consumer-dominated in FAERS versus physician-dominated in JADER), sex (female predominance in VigiBase, FAERS and VAERS, versus near-parity in JADER), and the influence of COVID-19 vaccination campaigns, which disproportionately affected VigiBase and VAERS. Time-series trends were analysed by comparing reports pre and post 1 January 2015, excluding COVID-19 vaccine reports. Changes in reporting patterns of therapeutic subgroups and adverse events since 2015 were consistent across SRSs with some region-specific variations.DiscussionBy highlighting differences between SRSs, this manuscript serves as a reference for researchers and regulators.”