Federated Automated Survey Tool (FAST)

摘要/摘要 急性病和慢性病领域的公共卫生ffi主要依靠调查数据。 收集有关疾病流行率、行为模式、风险人群、风险概率和疾病的信息 进步。传统的调查受到许多已知限制的制约，例如受访者不情愿 参与、社会期望偏差、问卷设计、数据收集和可获得性之间的滞后时间 结果，以及由于相关的执行费用而断断续续地报道重要专题。此外，疾病 控制专家和政策制定者无法访问实时数据和电子ffi工具来提供情景感知 与为疾病监测和项目管理而实施的调查相比较。其含义是 对环境和社区没有及时和更广泛的了解，ff缺乏代表性 以及用于推动政策和干预措施的评估和数据之城的人口统计数据。 拟议的联邦自动化测量工具(FAST)将作为Barron之间的协作开发 协会，公司，乔治梅森大学和弗吉尼亚大学的研究人员。FAST将成为一种分析 可供公共卫生组织、ffi首席执行官、临床护理调查人员、机构管理人员和 其他人则更容易调查有关急性和慢性病的目标队列(例如，在fl流感、冠状病毒、 高血压等。)以及其他指标(例如，抑郁症患病率、烟草使用、药物滥用等)通过 利用社交媒体(如Twitter)或其他网络/电子数据。基于自动化和可定制的 调查员的投入，拟议的FAST平台将促进建立适当的审讯 社交媒体和网络数据，为回答用户发起的问题提供前瞻性和纵向的见解。 FAST分析平台将支持地方、国家和世界范围内的地理调查-以及 根据社交媒体和网络用户的推文、帖子、电子邮件、搜索和其他网络进行人口统计 数据和元数据。FAST平台将利用复杂的文本分析和新颖的调查结构，并 分析技术。调查结果将被自动分析，以获得洞察力并回答不同的问题 一组关于有针对性的地理和人口特征的fic流行率和严重程度估计的问题。 这些调查可以是One-off调查、干预前和干预后调查，或者在线、实时、纵向调查。AS 后者的一个例子是，学校管理人员可以跟踪国家或更本地化(即，地理标记)的学生 实时发布关于饮酒、吸毒、压力、抑郁或自杀等问题的社交媒体帖子，使 管理人员更好地为学生量身定制ff服务和/或检测干预需求。 FAST平台将采用一种整合的方法，使非专家能够相对轻松地 创建、管理和调查几乎所有群体的社交网络和电子数据。使用FAST、FULL 概率抽样技术的范围(例如，简单随机样本、分层随机样本等)将会是 可供最终用户使用，以及相应的估计方差和估计误差界限。

Summary/Abstract Public health officials within both acute and chronic disease realms have relied predominantly on survey data to gather information on disease prevalence, behavioral models, risk populations, risk probability, and disease progression. Conventional surveys are subject to a number of known limitations, such as respondents' reluctance to participate, social desirability biases, lag time between questionnaire design, data collection, and availability of results, and intermittent coverage of important topics due to associated implementation costs. Further, disease control experts and policy makers lack access to real-time data and efficient tools to provide contextual awareness vis-à-vis surveys that are implemented for disease surveillance and program management. The implications of not having a timely and broader understanding of the environment and community affects the representativeness and demographic specificity of assessments and of the data used to drive policies and interventions. The proposed Federated Automated Survey Tool (FAST) will be developed as a collaboration among Barron Associates, Inc., George Mason University, and University of Virginia researchers. FAST will be an analytics platform that can be used by public health officials, clinical care investigators, institutional administrators, and others to more easily survey targeted cohorts regarding acute and chronic diseases (e.g., influenza, coronavirus, high blood pressure, etc.) and other indicators (e.g., depression prevalence, tobacco use, substance abuse, etc.) by harnessing social media (e.g., Twitter) or other web/electronic data. Based on both automated and tailorable investigator inputs, the proposed FAST platform will facilitate the construction of appropriate interrogations of social media and web data to yield prospective and longitudinal insights to answer user-initiated questions. The FAST analytics platform will enable local, national, and worldwide surveys on geographically- and demographically-targeted social media and web users based on their Tweets, posts, emails, search, and other web data and metadata. The FAST platform will utilize sophisticated text analytics and novel survey construction and analysis techniques. The survey results will then be analyzed automatically to gain insights and answer a diverse set of questions regarding targeted geographic- and demographic-specific prevalence and severity estimates. These can be one-off surveys, pre- and post-intervention surveys, or online, real-time, longitudinal surveys. As an example of the latter, school administrators could track national or more localized (i.e., geo-tagged) student social media posts in real time regarding issues such as drinking, drug use, stress, depression, or suicide, enabling administrators to better tailor services offered to students and/or detect the need for interventions. The FAST platform will employ a consolidated approach that makes it relatively easy for non-experts to create, administer, and survey social network and electronic data of nearly any cohort. With FAST, the full range of probability sampling techniques (e.g., simple random samples, stratified random samples, etc.) will be available to end-users, along with the corresponding estimated variance and bound on the error of the estimate.