Identifying Vulnerable Communities for Infectious Disease Outbreaks

项目总结 新冠肺炎疫情对美国种族和少数民族群体造成的不平等伤害突显出 脆弱的社区需要公共卫生官员的独特关注来解决健康差距问题 源于累积的不公正历史。与美国白人相比，美国黑人和西班牙裔美国人 以及原住民，由于新冠肺炎，住院的几率和死亡率都增加了- 19.快速、有重点的公共卫生应对对于未来的疫情防备是必要的，特别是在 更容易感染疾病的少数群体。人工智能(AI)已被用于预测 潜在的疾病暴发；然而，人工智能的一个分支--机器学习(ML)尚未在 识别易受伤害的人群和有疾病暴发风险的服务不足的社区，并跟踪 邻里层面的风险异质性。此外，虽然疾病发病率通常是以 县或邮政编码级别，了解社区传播中社区之间风险的异质性 疾病的发生需要更细粒度的地理单元来进行分析。为此，流行病学、地理空间和 机器学习工具将根据当地需求快速准确地识别易受影响的社区 在传染病暴发期间实现卫生公平势在必行。在目标1中，我们将探索关联 和呼吸道传染病发病率之间的趋势(例如。流感、肺结核、百日咳和新冠肺炎- 19)、疫苗接种覆盖率(MMR、DTaP、HPV和流感)，以及考虑到社会经济劣势 费城的地理。将使用地区剥夺指数和社会脆弱性指数来衡量 社会经济上的劣势。泊松和线性回归模型将被用来找出 传染病发病率、疫苗接种覆盖率低以及健康的社会决定因素。贝叶斯空间 将使用回归建模来评估受影响的脆弱社区比例的变化 并根据社区层面的因素确定疫苗接种覆盖面是否存在差距。在……里面 目标2，我们将训练一个基于地理信息系统(GIS)的ML模型，适合于聚合的地理空间 疾病、疫苗接种和来自目标1的健康数据的社会决定因素，并测试其预测能力 费城新冠肺炎案例数据。我们的目标是评估基于地理信息系统的ML模型的预测能力 关于确定公共卫生干预的领域。这项创新的研究将帮助我们预测 未来传染病暴发的风险，并帮助及时识别易感人群，以指导公众 卫生资源，这将对未来传染病的应急准备工作非常有用 疫情爆发。随附的培训计划包括授课和体验式学习机会，以及 将使申请者发展成为独立调查员所需的技能和经验 传染病领域的应用流行病学家。

PROJECT SUMMARY The COVID-19 pandemic’s unequal toll on racial and ethnic minority groups in the United States underscored that vulnerable communities need unique attention from public health officials to address health disparities stemming from a cumulative history of injustices. Compared to white Americans, Black and Hispanic Americans as well as indigenous populations have increased odds of hospitalization and higher deaths rates due to COVID- 19. A rapid, focused public health response is necessary for future outbreak preparedness, especially among minority populations that are more vulnerable to disease. Artificial Intelligence (AI) has been used to predict potential disease outbreaks; however, machine learning (ML), a branch of AI, has yet to be broadly used in identifying vulnerable populations and underserved communities at risk for disease outbreaks and track heterogeneities in risks at the neighborhood level. Furthermore, while disease incidence is often calculated at a county or zip code level, understanding heterogeneities in risk among neighborhoods in community transmission of diseases requires a more granular geographic unit for analysis. To this end, epidemiologic, geospatial, and machine learning tools to rapidly and accurately identify vulnerable neighborhoods based on local needs will be imperative to achieve health equity during infectious disease outbreaks. In Aim 1, we will explore associations and trends between respiratory infectious disease incidence (ex. influenza, tuberculosis, pertussis, and COVID- 19), vaccination coverage (MMR, DTaP, HPV, and influenza), and socioeconomic disadvantage considering geography in Philadelphia. Area Deprivation Index and Social Vulnerability Index will be used to measure socioeconomic disadvantage. Poisson and linear regression models will be used to find associations between infectious disease incidence, low vaccination coverage, and social determinants of health. Bayesian spatial regression modeling will be used to assess the change in the proportion of vulnerable communities affected by infectious diseases and identify any gaps in vaccination coverage differentially by neighborhood-level factors. In Aim 2, we will train a geographic information system (GIS)-based ML model, fit to the aggregated geospatial disease, vaccination, and social determinants of health data from Aim 1, and test its predictive capability on Philadelphia COVID-19 case data. Our goal will be to assess the predictive capability of GIS-based ML models on identifying areas for public health intervention. This innovative research will help us predict neighborhoods at risk of future infectious disease outbreaks and aid in timely identification of vulnerable populations to guide public health resources, which would be very useful for emergency preparedness efforts for future infectious disease outbreaks. The accompanying training plan consists of both didactic and experiential learning opportunities, and will enable the applicant to develop the skills and experience necessary to become an independent investigator and applied epidemiologist in the field of infectious diseases.