Development of a vaccine informatics system and its application to identifying the impact of vaccine debate on immunization rates during a global pandemic

疫苗信息学系统的开发及其在全球大流行期间确定疫苗辩论对免疫率的影响的应用

• 批准号: 10451553
• 负责人: Young Anna Argyris
• 金额: $ 15.69万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10451553
• 关键词: Address; Adolescent; Affect; Algorithms; Belief; COVID-19; COVID-19 pandemic; COVID-19 vaccine; Child; Classification; Collection; Data; Data Set; Disease; Disease Outbreaks; Dose; Drug Industry; Educational process of instructing; Epidemic; Frequencies; Fright; Goals; Government; Health; Health Care Costs; Human; Human Papilloma Virus Vaccination; Human Papilloma Virus Vaccine; Human Papilloma Virus-Related Malignant Neoplasm; Human Papillomavirus; Immunization; Individual; Informatics; Informed Consent; Intervention; Knowledge; Knowledge Discovery; Lead; Machine Learning; Malignant neoplasm of cervix uteri; Measles; Medicine; Methodology; Methods; Misinformation; Modeling; Morbidity - disease rate; Movement; Mumps; Neighborhoods; Noise; Outcome; Performance; Population; Prevention; Public Health; Research; Respondent; Risk; Rubella; Safety; Solid; Statistical Models; Surveys; System; Techniques; Testing; Time; Training; Vaccination; Vaccines; complex data; cost; data management; deep learning; deep learning algorithm; digital; distrust; flu; geographic difference; heterogenous data; image processing; influenza epidemic; influenza virus vaccine; innovation; insight; machine learning algorithm; multimodality; natural language; novel vaccines; pandemic disease; prevent; response; social media; statistics; uptake; vaccine acceptance; vaccine development; vaccine discovery; vaccine refusal; vaccine trial; web portal

Vaccine debate has been on social media for more than a decade, and a surge of anti-vaccine activities on social media has been detected during prior disease outbreaks. Nonetheless, how this debate changes and impacts the uptake rates for crucial vaccines during the COVID-19 pandemic remains unknown. The long-term goal is to counteract the negative impact of misinformation on digital platforms that threatens public health. The overall objectives of this application are to develop a publicly accessible vaccine informatics system to track vaccine debate, and to test the impact of vaccine debate on COVID-19 (if developed by 2021), flu, and HPV immunization rates during the onset of a global pandemic. The central hypothesis is that vaccine debate will increase and become more negative during the pandemic, leading to lower vaccine uptake rates. The rationale for this project is that discovering how vaccine debate changes and influences vaccine uptake rates during a pandemic will be critically important for managing and preventing disease spread. The central hypothesis will be tested by pursuing two specific aims: 1) Develop a vaccine informatics system to identify the frequency and valence of vaccine debate during and following the pandemic compared to the pre-pandemic baseline; and 2) Apply this system to identify the causal impact of vaccine debate on immunization rates during the pandemic. Under the first aim, ~1 million social media posts will be collected, and a deep-learning algorithm for classifying multimodal social media posts will be developed. This algorithm will address potential bias and noise in human annotations of vaccine debate that is increasingly politicized. The classification results will be tabulated in a Web portal so that daily and weekly statistics about pro- and anti-vaccine posts will be readily available. Under the second aim, a multimethod approach will be proposed that resolves the current barriers in research on vaccine refusal. This approach will use a survey of 2,000 individuals who represent the US population. The survey responses will be combined with the respondents' prior engagement with vaccine debate retrospectively collected from social media. These engagement data will be then classified by the machine- learning algorithm developed in Aim 1. This research is innovative because it proposes a robust co-teaching framework for addressing noisy human annotations of vaccine debate. It also proposes a statistical modeling technique that involves heterogenous metrics obtained from a multi-method approach for hypothesis testing. These innovations are timely and urgent as the current time presents a rare opportunity to identify the impact of vaccine debate on public health during the onset of a global pandemic. The feasibility of this proposed research is clear from the solid preliminary datasets collected from 2018-2020 that establish the pre-pandemic baseline. The proposed research is significant because it will produce a public barometer of vaccine debate and provide a methodological breakthrough in uncovering the reasoning behind refusing crucial vaccines during the global pandemic.

关于疫苗的争论已经在社交媒体上持续了十多年，社交媒体上的反疫苗活动激增 在之前的疾病爆发期间就已经发现了社交媒体。尽管如此，这场辩论如何变化以及 对 COVID-19 大流行期间关键疫苗接种率的影响仍不得而知。长期来看 目标是抵消数字平台上威胁公共健康的错误信息的负面影响。这 该应用程序的总体目标是开发一个可公开访问的疫苗信息学系统来跟踪 疫苗辩论，并测试疫苗辩论对 COVID-19（如果在 2021 年开发出来）、流感和 HPV 的影响 全球大流行爆发期间的免疫率。中心假设是疫苗辩论将 在大流行期间增加并变得更加消极，导致疫苗接种率降低。理由 该项目的目的是发现疫苗辩论如何改变并影响疫苗接种率 大流行对于管理和预防疾病传播至关重要。中心假设将 通过追求两个具体目标进行测试：1）开发疫苗信息学系统来确定频率和 与大流行前的基线相比，大流行期间和之后的疫苗辩论的效价；和 2) 应用该系统来确定大流行期间疫苗辩论对免疫率的因果影响。 第一个目标将收集约 100 万条社交媒体帖子，并采用深度学习算法进行分类 将开发多模式社交媒体帖子。该算法将解决人类潜在的偏见和噪音问题 对日益政治化的疫苗争论的注解。分类结果将制成表格 门户网站，以便可以轻松获得有关支持和反对疫苗帖子的每日和每周统计数据。在下面 第二个目标是提出一种多方法方法来解决目前研究中的障碍 拒绝接种疫苗。该方法将对代表美国人口的 2,000 人进行调查。这 调查回复将与受访者之前参与疫苗辩论的情况相结合 从社交媒体回顾收集。然后，这些参与数据将由机器进行分类 目标 1 中开发的学习算法。这项研究具有创新性，因为它提出了一种稳健的协同教学 用于解决疫苗争论的嘈杂人类注释的框架。它还提出了一个统计模型 涉及从假设检验的多方法方法获得的异质指标的技术。 这些创新是及时和紧迫的，因为当前是确定其影响的难得机会 全球大流行爆发期间关于公共卫生的疫苗辩论。本提议的可行性 从 2018 年至 2020 年收集的可靠初步数据集中可以清楚地看出，这些数据确定了大流行前的情况 基线。拟议的研究意义重大，因为它将产生疫苗辩论的公共晴雨表 并为揭示拒绝关键疫苗背后的原因提供方法上的突破 在全球大流行期间。

项目成果

Do social media campaigns foster vaccination adherence? A systematic review of prior intervention-based campaigns on social media.

• DOI: 10.1016/j.tele.2022.101918
• 发表时间: 2023-01
• 期刊: TELEMATICS AND INFORMATICS
• 影响因子: 8.5
• 作者: Argyris, Young Anna;Nelson, Victoria R.;Wiseley, Kaleigh;Shen, Ruoyu;Roscizewski, Alexa
• 通讯作者: Roscizewski, Alexa

Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media.

使用深度学习来识别促进或抑制社交媒体上反对和支持疫苗内容传播的语言特征。

• DOI: 10.1109/icdh55609.2022.00025
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Digital Health (IEEE ICDH 2022) : proceedings : hybrid conference, Barcelona, Spain, 11-15 July 2022. International Conference on Digital Health (2022 : Barcelona, Spain; Online)
• 作者: Argyris,YoungAnna;Zhang,Nan;Bashyal,Bidhan;Tan,Pang-Ning
• 通讯作者: Tan,Pang-Ning