Identifying Vulnerable Communities for Infectious Disease Outbreaks

确定传染病爆发的脆弱社区

基本信息

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
项目类别：
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

项目摘要

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社区需要公共卫生官员的特别关注来解决健康差距问题与美国白人、黑人和西班牙裔美国人相比，这是源于累积的不公正历史。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，特别是在人工智能（AI）已被用来预测更容易感染疾病的少数群体。潜在的疾病爆发；然而，人工智能的一个分支机器学习（ML）尚未广泛应用于识别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此外，疾病发病率通常是按社区水平计算的。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为此，需要更精细的地理单位进行流行病学、地理空间和分析。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）以及考虑到社会经济劣势费城的地理状况将采用地区剥夺指数和社会脆弱性指数来衡量。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贝叶斯空间决定因素。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。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。这项创新研究将帮助我们预测公共卫生干预领域。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。