Hyun Lim Su, Hersi Mona, Krishnan Ramya, Montroy Joshua, Rook Bonnie, Farrah Kelly, Chung Yung-En, Stevens Adrienne, Zafack Joseline, Wong Eva, Forbes Nicole, Killikelly April, Young Kelsey, Tunis Matthew
Centre for Immunization Programs, Infectious Disease and Vaccine Program Branch, Public Health Agency of Canada, 130 Colonnade Rd S, Nepean, ON, Canada, K2E1B6.
Vaccine X. 2024 Oct 24;21:100575. doi: 10.1016/j.jvacx.2024.100575. eCollection 2024 Dec.
The COVID-19 pandemic resulted in a rapid accumulation of novel vaccine research evidence. As a means to monitor this evidence, the Public Health Agency of Canada (PHAC) created the vidence etraction eam for esearch nalysis (), which contributed to situational awareness in Canada through a bibliographic repository used to support decision-making by the National Advisory Committee on Immunization. We describe the process by which this literature was identified and catalogued, and provide an overview of characteristics in the identified literature.
To expedite the process, PHAC leveraged an artificial intelligence (AI) tool to assist in the screening and selection of relevant articles. Literature search results were initially screened by AI, then manually reviewed for relevance. Relevant articles were tagged using controlled vocabulary and stored in a bibliographic repository. This repository was analyzed to identify trends in vaccine research over time according to several key characteristics.
As of December 31, 2023, EXTRA's repository contained 19,050 articles relevant to PHAC's immunization mandate. The majority of these articles (63.9 %) were identified between August 2021 and January 2023, with an average of 20 relevant articles added daily during this period. Nearly 14,000 articles reported on mRNA vaccines. Safety outcomes were most frequently reported (n = 8,289), followed by immunogenicity (n = 7,269) and efficacy/effectiveness (n = 3,246). COVID-19 vaccine literature output started to decrease in mid-2023, two years after the initial dramatic increase in mid-2021.
This hybrid (AI and human) approach was critical for PHAC situational awareness and the development of timely vaccine guidance in Canada during the COVID-19 pandemic. Given the volume of data and analyses required, the AI-augmented processes made this massive undertaking manageable. Analysis of COVID-19 vaccine research patterns supports projections of research volume, type, and rate that will help predict resourcing and information needs to plan future emergency vaccine guidance activities.
新冠疫情导致新型疫苗研究证据迅速积累。作为监测这些证据的一种方式，加拿大公共卫生局（PHAC）创建了用于研究分析的证据提取团队（EXTRA），该团队通过一个文献库提高了加拿大的态势感知能力，该文献库用于支持国家免疫咨询委员会的决策。我们描述了识别和编目这些文献的过程，并概述了已识别文献的特征。
为加快这一过程，PHAC利用人工智能（AI）工具协助筛选和选择相关文章。文献搜索结果首先由AI进行筛选，然后人工审查其相关性。相关文章使用受控词汇进行标记，并存储在文献库中。对该文献库进行分析，以根据几个关键特征确定疫苗研究随时间的趋势。
截至2023年12月31日，EXTRA的文献库包含19,050篇与PHAC免疫任务相关的文章。这些文章中的大多数（63.9%）是在2021年8月至2023年1月期间识别的，在此期间平均每天新增20篇相关文章。近14,000篇文章报道了mRNA疫苗。安全性结果报告最为频繁（n = 8,289），其次是免疫原性（n = 7,269）和有效性/效果（n = 3,246）。新冠疫苗文献产出在2023年年中开始下降，此时距离2021年年中最初的急剧增长已有两年时间。
这种混合（AI和人工）方法对于PHAC在新冠疫情期间的态势感知以及在加拿大制定及时的疫苗指南至关重要。鉴于所需的数据量和分析量，AI增强的流程使这项艰巨任务变得可控。对新冠疫苗研究模式的分析支持了对研究数量、类型和速度的预测，这将有助于预测规划未来紧急疫苗指南活动所需资源和信息。
