Identifying Vulnerable Communities for Infectious Disease Outbreaks

确定传染病爆发的脆弱社区

• 批准号: 10464066
• 负责人: Tuhina Srivastava
• 金额: $ 4.93万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10464066
• 关键词: Active Learning; Address; Affect; Area; Artificial Intelligence; Attention; Black American; Black Populations; Black race; COVID-19; COVID-19 outbreak; COVID-19 pandemic; COVID-19 risk; COVID-19 susceptibility; COVID-19 vaccination; Censuses; Code; Communicable Diseases; Communities; Community Health; County; Data; Data Set; Death Rate; Disadvantaged; Disease; Disease Outbreaks; Educational Status; Emergency Situation; Epidemiologist; Epidemiology; Essential worker; Ethnic Origin; Future; Geographic Distribution; Geographic Information Systems; Geography; Goals; Health; Health Resources; Heterogeneity; Hispanic; Hispanic Americans; Hospitalization; Housing; Human Papillomavirus; Incidence; Income; Indigenous; Infection; Influenza; Latino; Latino Population; Linear Regressions; Machine Learning; Maps; Measures; Methods; Minority Groups; Modeling; Native-Born; Neighborhoods; Occupational; Pattern; Persons; Pertussis; Philadelphia; Population; Populations at Risk; Poverty; Public Health; Race; Readiness; Recording of previous events; Research; Research Personnel; Resource Allocation; Respiratory Disease; Respiratory Tract Infections; Risk; Risk Factors; SARS-CoV-2 infection; Testing; Time; Time trend; Training; Tuberculosis; United States; Vaccination; Validation; Vulnerable Populations; age group; base; caucasian American; community transmission; cost; data registry; deprivation; disease transmission; disorder risk; doctoral student; ethnic minority population; experience; health care availability; health data; health disparity; health equity; hospitalization rates; improved; indexing; influenza virus vaccine; innovation; machine learning algorithm; machine learning method; machine learning model; neighborhood disadvantage; novel; people of color; predictive modeling; public health intervention; racial and ethnic; racial minority; respiratory; response; risk prediction; skills; social determinants; social health determinants; social vulnerability; socioeconomic disadvantage; socioeconomics; stem; tool; trend; underserved community; vulnerable community

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.

项目概要 COVID-19 大流行对美国少数族裔群体造成的不平等影响凸显 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 源于累积的不公正历史。与美国白人相比，黑人和西班牙裔美国人 以及土著居民因新冠肺炎而增加了住院几率和更高的死亡率 19. 快速、有针对性的公共卫生应对措施对于未来的疫情应对是必要的，特别是在 少数民族人口更容易感染疾病。人工智能（AI）已被用来预测 潜在的疾病爆发；然而，作为人工智能的一个分支，机器学习（ML）尚未广泛应用于 确定面临疾病爆发风险的弱势群体和服务不足的社区，并进行追踪 邻里层面风险的异质性。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位进行分析。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脆弱社区 在传染病爆发期间实现健康公平势在必行。在目标 1 中，我们将探索关联 呼吸道传染病（例如流感、肺结核、百日咳和新冠肺炎）发病率之间的趋势 19)、疫苗接种覆盖率（MMR、DTaP、HPV 和流感）以及考虑到的社会经济劣势 费城的地理。将采用地区剥夺指数和社会脆弱性指数来衡量 社会经济劣势。泊松和线性回归模型将用于查找之间的关联 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 将使用回归模型来评估受影响的脆弱社区比例的变化 传染病，并根据社区层面的因素确定疫苗接种覆盖率的差距。在 目标 2，我们将训练基于地理信息系统 (GIS) 的 ML 模型，以适应聚合的地理空间 目标 1 中的疾病、疫苗接种和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，指导公众 卫生资源，这对于未来传染病的应急准备工作非常有用 爆发。随附的培训计划包括教学和体验式学习机会，以及 将使申请人能够发展成为独立调查员所需的技能和经验 以及传染病领域的应用流行病学家。