Leveraging artificial intelligence and social innovation to reduce disparities in COVID-19 testing among African Americans

摘要 大流行性疲劳-一种现象，其特征是缺乏遵循推荐的保护行为的动力 随着时间的推移出现，并受到一个人的情绪，经验和感知的影响-威胁我们的能力 来结束COVID-19疫情疫苗诱导的免疫力减弱，突破性感染，新的变异， 不确定性都造成了流行病疲劳。这些持续的挑战突出表明， COVID-19缓解策略，包括COVID-19检测，以实现长期的疫情控制。 虽然流行病疲劳是对长期公共卫生危机的预期和自然反应， 我们有能力保障服务不足和医疗和/或社会弱势群体的安全，包括 非裔美国人鉴于完全根除或消除是不可行的，科学家和公共卫生 官员们正集中精力采取控制措施，使COVID-19成为地方病。为了达到地方病的地位，我们必须 确定并解决弱势人群中COVID-19检测的障碍，包括大流行疲劳。 此外，我们必须推进传播科学干预，使我们能够确定 可以利用针对COVID-19感知风险的信息的呈现来增加 COVID-19测试行为，并采用有效的沟通策略来减轻暴露的影响 对测试接受度和吸收度的错误信息。以能力机会动机-行为为导向 和少数民族健康和健康差异研究框架，这项研究将利用参与性研究 方法，人工智能和基础设施，从正在进行的社区参与COVID-19缓解 研究：1）举办一个设计通村，开发深度学习计算机动画，能够传达 COVID-19检测和促进非裔美国人在社区环境中接受COVID-19检测的重要性， NC. 2)确定深度学习计算机动画干预（与对照相比）是否改善了COVID-19 使用1：1随机实验测试摄取。研究结果将确定有效的COVID-19检测 针对非裔美国人的推广信息，有可能推广到其他关键人群。

Abstract Pandemic fatigue—a phenomenon characterized by a demotivation to follow recommended protective behaviors that emerges over time and is affected by one’s emotions, experiences and perceptions—threatens our ability to end the COVID-19 pandemic. Waning vaccine-induced immunity, breakthrough infections, new variants, and uncertainty all contribute to pandemic fatigue. These ongoing challenges highlight the importance of sustaining COVID-19 mitigation strategies, including COVID-19 testing, over the long run to achieve pandemic control. While pandemic fatigue is an expected and natural response to a prolonged public health crisis, it compromises our ability to keep members of underserved and medically and/or socially vulnerable populations safe, including African Americans. Given that complete eradication or elimination are not feasible, scientists and public health officials are focused on control measures to make COVID-19 endemic. To achieve endemic status, we must identify and address barriers to COVID-19 testing within vulnerable populations, including pandemic fatigue. Moreover, we must advance communication science interventions that enable us to determine how variations in the presentation of messages targeting perceived risk for COVID-19 can be leveraged to increase motivation for COVID-19 testing behaviors, and employ effective communication strategies to mitigate the impact of exposure to misinformation on testing acceptance and uptake. Guided by the Capability Opportunity Motivation—Behavior and Minority Health and Health Disparities Research Frameworks, this study will leverage participatory research methods, artificial intelligence, and infrastructure from ongoing community-engaged COVID-19 mitigation research to: 1) Host a design-a-thon to develop deep learning computer animations capable of conveying the importance of COVID-19 testing and promoting its uptake in community settings among African Americans in NC. 2) Determine whether a deep learning computer animation intervention (vs a control) improves COVID-19 testing uptake using a 1:1 randomized experiment. Study results will identify effective COVID-19 testing promotion messages for African Americans with the potential for generalization to other key populations.