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

疫苗辩论已经在社交媒体上持续了十多年， 社交媒体在以前的疾病爆发期间已经被发现。然而，这场辩论如何改变， 在COVID-19大流行期间，对关键疫苗接种率的影响仍然未知。长期 目标是抵消数字平台上威胁公共卫生的错误信息的负面影响。的 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系统，以跟踪 疫苗辩论，并测试疫苗辩论对COVID-19（如果到2021年开发），流感和HPV的影响 在全球大流行病爆发期间的免疫率。核心假设是，疫苗辩论将 在大流行期间，疫苗接种率增加并变得更加消极，导致疫苗接种率降低。的理由 这个项目的目的是发现疫苗辩论如何改变和影响疫苗接种率， 大流行对于管理和预防疾病传播至关重要。核心假设将 通过追求两个具体目标进行测试：1）开发疫苗信息系统，以确定频率和 与大流行前基线相比，大流行期间和之后的疫苗效价辩论;以及2） 应用这一系统来确定大流行期间疫苗辩论对免疫率的因果影响。 在第一个目标下，将收集约100万个社交媒体帖子，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。该算法将解决潜在的偏见和噪音在人类 对疫苗争论的注解越来越政治化。分类结果将以表格形式载于 门户网站，以便随时提供有关支持和反对疫苗帖子的每日和每周统计数据。下 第二个目标，将提出一种多方法的方法，解决目前的研究障碍， 拒绝接种疫苗这种方法将使用代表美国人口的2,000人的调查。的 调查结果将与受访者之前参与疫苗辩论的情况相结合 从社交媒体上回顾性地收集。这些交战数据将被机器分类- 在Aim 1中开发的学习算法。这项研究是创新的，因为它提出了一个强大的合作教学 框架解决嘈杂的人类注释的疫苗辩论。它还提出了一个统计模型， 一种技术，涉及从多方法方法中获得的异质指标，用于假设检验。 这些创新是及时和紧迫的，因为当前是确定影响的难得机会 在全球流行病爆发期间，关于公共卫生的疫苗辩论。建议的可行性 从2018-2020年收集的初步数据集可以清楚地看到， 基线。这项拟议中的研究意义重大，因为它将产生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上的突破 在全球大流行期间。

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.