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.

疫苗争论在社交媒体上已经持续了十多年，反对疫苗的活动在 社交媒体在之前的疾病暴发期间被检测到。尽管如此，这场辩论是如何改变的，以及 新冠肺炎大流行期间关键疫苗的接种率仍不得而知。长期的 目标是抵消数字平台上威胁公众健康的错误信息的负面影响。这个 这个应用程序的总体目标是开发一个公众可访问的疫苗信息学系统来跟踪 疫苗辩论，并测试疫苗辩论对新冠肺炎(如果到2021年发展)、流感和人乳头状瘤病毒的影响 全球大流行爆发期间的免疫接种率。核心假设是疫苗辩论将 在大流行期间增加并变得更负，导致较低的疫苗接种率。其基本原理是 对于这个项目来说，发现疫苗辩论如何改变和影响疫苗接种率在 大流行对于管理和防止疾病传播至关重要。中心假说将 通过追求两个具体目标进行测试：1)开发疫苗信息学系统，以确定疫苗的频率和 与大流行前基线相比，大流行期间和大流行后疫苗辩论的价值；2) 应用这一系统来确定大流行期间疫苗辩论对免疫接种率的因果影响。 在第一个目标下，将收集约100万条社交媒体帖子，并使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声问题 对疫苗辩论的诠释越来越政治化。分类结果将以表格形式列出 网站门户，这样就可以随时获得支持和反对疫苗的帖子的每日和每周统计数据。在……下面 第二个目标，将提出一种多方法方法，以解决目前研究中的障碍 拒绝接种疫苗。这一方法将使用对代表美国人口的2000人的调查。这个 调查答复将与受访者先前参与疫苗辩论的情况相结合 从社交媒体上追溯收集的数据。然后，这些交战数据将由机器分类- 在目标1中开发的学习算法。这项研究具有创新性，因为它提出了一种健壮的合作教学 解决人类对疫苗争论的嘈杂注释的框架。并提出了一种统计建模方法 一种技术，涉及从用于假设检验的多方法方法获得的异质指标。 这些创新是及时而紧迫的，因为当前是确定影响的难得机会 在全球大流行爆发期间，疫苗辩论在公共卫生方面发挥了重要作用。这一提议的可行性 从2018-2020年收集的坚实的初步数据集中可以清楚地看到，这些数据确立了大流行前的情况 基线。这项拟议的研究意义重大，因为它将成为疫苗辩论的公共晴雨表 并在揭示拒绝关键疫苗背后的原因方面提供了方法论上的突破 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