The COVID-19 pandemic led to an unprecedented acceleration in vaccine development, accompanied by a rapidly expanding body of evidence. Yet, pregnant individuals and children were frequently excluded from early trials, leaving crucial gaps in safety and effectiveness data for these at-risk groups [1]. This scenario highlighted the need for timely, high-quality evidence to guide vaccine development and equitable access [2,3,4,5,6]. Although the acute phase of the pandemic has passed, the lessons it taught remain vital—particularly the importance of generating robust benefit-risk assessments throughout the vaccine lifecycle. Early stages require insight from preclinical and indirect data (e.g., platform technologies or adjuvants). At the same time, subsequent phases must synthesize evolving clinical trial results and real-world evidence to inform policy and public trust [7].
Living systematic reviews (LSRs) offer an effective framework for this process. LSRs support dynamic decision-making across research, regulation, and implementation by continuously updating literature searches and evidence synthesis. They are particularly useful in under-resourced settings where data gaps persist [8]. Our approach consolidates data from high-income and low- to middle-income countries, shedding light on critical gaps in under-resourced settings. By offering a user-friendly evidence map and real-time meta-analyses based on trusted sources, we aim to support decision-making across vaccine development pipelines and implementation strategies worldwide.
To facilitate the rapid synthesis of emerging evidence, we developed an integrated web platform for current and future infectious disease threats. Initially focused on COVID-19 vaccines in pregnancy, our LSR has expanded to include vaccines for chikungunya, Lassa fever, Mpox, and Disease X, particularly for pregnant individuals and children. This initiative is dedicated to continuously gathering and evaluating data on vaccine safety, efficacy/effectiveness, and immunogenicity.
We developed an integrated online platform initially focused on COVID-19 vaccines in pregnancy to address this need. This LSR has since expanded to include vaccines for chikungunya, Lassa fever, Mpox, and Disease X, focusing on pregnancy and childhood populations. This issue contains detailed protocols for two new LSRs, focusing on chikungunya (CRD42024514513, CRD42024516754) [9] and Lassa fever (CRD42024554330, CRD42024556977) [10], which will serve as foundational frameworks for synthesizing emerging evidence. These diseases, prioritized for their public health impact, exemplify the importance of real-time, context-specific evidence to inform future responses.
Chikungunya has recently surged in the Americas, with over 214,000 cases reported in early 2023 [11]. The virus presents a significant burden due to its potential for severe congenital infections and long-term neonatal complications [12]. In August 2023, the first Chikungunya vaccine IXCHIQ™ (VLA1553, Valneva live attenuated vaccine) was approved in the United States [13, 14], marking a pivotal step in addressing this disease. More recently, on February 14, 2025, the U.S. FDA approved VIMKUNYA™ (Bavarian Nordic Chikungunya Vaccine, Recombinant) for individuals aged 12 and older, presenting a major milestone in Chikungunya prevention [15]. This underscores the urgent need for further data on vaccine safety and efficacy, particularly in pregnant women and children, as the risk of chronic morbidity and adverse pregnancy outcomes remains a significant concern. Moreover, climate change is expanding the range of mosquito-borne diseases, increasing the urgency of targeted interventions, including vaccines.
Lassa fever, caused by the Lassa virus and transmitted through contact with rodents, is endemic in West Africa [16] and has been designated as a priority for research and development by the World Health Organization (WHO) [3]. The disease is associated with an estimated 5,000 deaths annually and high mortality rates, particularly in pregnant women (29% maternal mortality in the third trimester) [17, 18] and neonates (87% fetal/neonatal death) [19]. Despite the urgent need for immunization, no approved vaccine is currently available. However, clinical trials are underway, with phase 2 studies in progress to evaluate vaccine candidates [20]. Ensuring the collection of robust safety and efficacy data, especially in children and pregnant women, remains a critical priority.
Evidence synthesis approach
Drawing on our previous experience with a LSR of COVID-19 vaccines administered during pregnancy, we have broadened the project to encompass vaccines for pregnant persons and children against other emerging infectious diseases (https://www.safeinpregnancy.org/) [21, 22]. Our review expanded to include vaccines against pathogens such as chikungunya, Lassa fever, and mpox, which present significant risks in various regions and have the potential for broader transmission. To gather pertinent information, we examine data about vaccine platforms and develop flexible protocols and search strategies that can be swiftly adjusted to address emerging threats. It is important to note that COVID-19 remains a global public health concern and remains in our LSR platform, with immunization coverage during pregnancy still low in several regions [23]. Overcoming barriers to adopting new, effective vaccines poses an even more significant challenge.
Our methodological approach, fully described in the protocols published in this issue [9, 10], based on Cochrane methods for these LSRs and meta-analyses, follows several key steps outlined in our initial article [24]. First, we perform exhaustive searches of published and grey literature across multiple databases—including the Cochrane Library, MEDLINE, EMBASE, LILACS, and Chinese databases—ensuring that studies are captured without language restrictions over relevant periods. Second, after a title and abstract screening accelerated by Nested Knowledge, pairs of authors independently select articles by full text. This web-based software, powered by artificial intelligence, facilitated the dual independent screening by a reviewer and robot screener after training the model with 50 records. Disagreements between humans and the robot were resolved by consensus of the whole review team [25]. They then extract data and assess the risk of bias of included studies, solving disagreements by consensus. We meticulously extract data using REDCap electronic data capture tools, focusing on key aspects such as study identification, participant characteristics, interventions, and outcomes. This approach ensures rigorous data quality control processes and enhances the reliability of our analyses.
Presentation of findings
The data is consolidated and visualized using PowerBI, which provides an interactive dashboard for exploration. It is available at https://www.safeinpregnancy.org/living-systematic-review/. This tool allows stakeholders—including policymakers, researchers, guideline developers, and clinicians—to explore the evidence interactively through filters (e.g., population, vaccine type, outcome) and to generate customized figures, tables, and maps. RShiny enables real-time, user-defined meta-analyses and generates forest plots with pooled estimates and confidence intervals [26]. Users can select the relevant outcomes based on the population of interest and apply filters or subgroups such as vaccine platform, vaccine doses, population, and comparators to show emerging vaccines’ overall safety and efficacy. The certainty of evidence from comparative studies is presented in Summary of Finding tables using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. It evaluates five key domains: risk of bias, inconsistency, indirectness, imprecision, and publication bias. Based on these domains, the certainty of evidence is rated as high, moderate, low, or very low, helping users interpret how much confidence they can place in the effect estimates [24, 27]. This thorough process is supported by meticulous quality control and validation measures to ensure the robustness and reliability of the findings.