Identifying Vulnerable Communities for Infectious Disease Outbreaks

项目摘要 COVID-19大流行对美国种族和少数民族群体造成的不平等影响凸显了 弱势群体需要公共卫生官员的特别关注，以解决健康差距问题， 是由累积的不公正历史造成的与白色美国人相比，黑人和西班牙裔美国人 以及原住民因COVID而住院的可能性增加，死亡率上升， 19.快速、集中的公共卫生应对措施对于未来的疫情准备是必要的，特别是在 更容易受到疾病影响的少数群体。人工智能（AI）已被用于预测 潜在的疾病爆发;然而，机器学习（ML），人工智能的一个分支，尚未被广泛用于 确定易受感染人群和服务不足社区面临疾病爆发风险， 社区层面的风险异质性。此外，虽然疾病发病率通常按 县或邮政编码级别，了解社区传播中邻里之间风险的异质性 需要一个更细粒度的地理单元进行分析。为此，流行病学、地理空间和 机器学习工具，根据当地需求快速准确地识别脆弱的社区， 在传染病爆发期间必须实现卫生公平。在目标1中，我们将探讨 以及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COVID- 19），疫苗接种覆盖率（MMR，DTaP，HPV和流感），以及考虑到社会经济不利因素 费城的地理。区域脆弱性指数和社会脆弱性指数将用于衡量 社会经济劣势。Poisson和线性回归模型将用于发现 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 回归模型将用于评估受影响的脆弱社区比例的变化， 传染病和查明疫苗接种覆盖率的差距，按社区一级的因素区分。在 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COVID-19病例数据。我们的目标是评估基于GIS的ML模型的预测能力 确定公共卫生干预领域。这项创新的研究将帮助我们预测社区， 未来传染病爆发的风险，并帮助及时识别脆弱人群，以指导公众 卫生资源，这将对今后传染病的应急准备工作非常有用 爆发。附带的培训计划包括教学和体验式学习机会， 将使申请人能够发展成为独立调查员所需的技能和经验 和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.