Developing a dynamic modeling framework for surveillance, prediction, and real-time resource allocation to reduce health disparities during Covid-19 and future pandemics

开发用于监测、预测和实时资源分配的动态建模框架，以减少 Covid-19 和未来大流行期间的健康差距

• 批准号: 10584876
• 负责人: Lior Rennert
• 金额: $ 68.12万
• 依托单位: CLEMSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-05 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10584876
• 关键词: Address; Age; American; Black American; COVID-19; COVID-19 mortality; COVID-19 pandemic; COVID-19 risk; COVID-19 testing; COVID-19 vaccination; COVID-19 vaccine; Cessation of life; Characteristics; Clinic; Clinical; Collaborations; Collection; Communicable Diseases; Communities; Complex; Data; Data Sources; Databases; Decision Making; Development; Disease; Disease Outcome; Disease Surveillance; Disparity; Emergency Situation; Emerging Communicable Diseases; Epidemiology; Foundations; Future; Geographic Locations; Goals; Health; Health Services Accessibility; Health system; Hispanic Americans; Hospitalization; Individual; Inequity; Infectious Disease Epidemiology; Information Systems; Infrastructure; Intervention; Knowledge; Location; Machine Learning; Measures; Modeling; Monitor; Morbidity - disease rate; Parameter Estimation; Pathway interactions; Phase; Population; Population Surveillance; Populations at Risk; Process; Public Health; Reduce health disparities; Resource Allocation; Resources; Risk; SARS-CoV-2 infection; Schedule; Science; South Carolina; Statistical Models; Structure; Surveys; Symptoms; System; Testing; Time; Translations; Underserved Population; Update; Vaccination; Vaccines; Work; barrier to care; contextual factors; coping; disease transmission; economic disparity; emerging pathogen; flexibility; future pandemic; health disparity; health inequalities; high risk; high risk population; improved; inequitable distribution; innovation; intervention delivery; medically underserved; medically underserved population; mobile health clinic; models and simulation; mortality; multilevel analysis; neighborhood disadvantage; pandemic disease; pandemic preparedness; predictive modeling; prevent; respiratory; rural Americans; severe COVID-19; sociodemographics; socioeconomics; tool; transmission process; underserved community; uptake; user-friendly; vaccine acceptance; vaccine effectiveness; vaccine hesitancy

Project Summary Black, Hispanic, and rural Americans are twice as likely to die from Coronavirus Disease 2019 (Covid-19). These health disparities have been fueled by inadequate access to essential resources throughout the pandemic. Such inequities are not unique to Covid-19. Over the past century, emerging infectious diseases have significantly perpetuated health disparities in underserved communities. The interconnected pathways leading to these disparities, including heterogeneous disease epidemiology, sociodemographic characteristics, and treatment access and uptake, remain understudied. Mobile health clinics (MHC) are an effective and versatile tool for reducing health disparities through timely delivery of interventions to medically underserved populations. However, the inability to effectively identify and prioritize high-risk communities has posed daunting challenges for MHC decision makers and has led to suboptimal allocation strategies. To help improve the efficiency of these field-level interventions and reduce health disparities during Covid-19 and future pandemics, our proposal seeks to develop a modeling toolkit to improve infectious disease surveillance and prediction in underserved populations and prioritize the delivery of essential resources to high-risk communities in real time. Our innovative, multilevel modeling framework will utilize statistical models, machine learning, compartment-based and agent-based models to reduce health disparities through 1) establishing a real-time data system feed for infectious disease surveillance and estimation of disease epidemiology in underserved communities 2) identifying at-risk populations for allocation of essential resources, 3) evaluating the complex interplay between sociodemographic and clinical characteristics, infectious disease epidemiology, modifiable health barriers, and intervention uptake in order to improve emergency planning during the Covid-19 pandemic and future health emergencies, and 4) establishing a modeling toolkit to inform delivery of essential resources to underserved communities in real-time. This will be accomplished through real-time integration of infectious disease outcome data, demographic, socioeconomic, and clinical characteristics, vaccine hesitancy surveys, community-level contextual factors, and data on structural barriers to health care for estimation of key input parameters in the dynamic simulation modeling framework. The framework we propose will be generalizable to other infectious diseases, where model inputs will be disease and location dependent for swift translation to other public health problems. To demonstrate the utility of our toolkit, our modeling framework will focus on delivery of Covid-19 mobile vaccination clinics to underserved populations in South Carolina (SC). Our proposal will improve pandemic planning by developing the modeling infrastructure for disease surveillance and understanding of infectious disease epidemiology in underserved communities, ultimately improving timely delivery of essential resources to those of greatest need. Covid-19 has claimed nearly 1 million American lives and has hospitalized over 4 million individuals through February 2022. Utilization of this toolkit by public health decision makers can prevent thousands of future Covid-19 deaths. Through adaptation of input data sources, our modeling framework is easily translatable to other infectious diseases and geographic regions and has potential to save many more lives in future pandemics.

项目摘要 黑人，西班牙裔和农村美国人死于2019冠状病毒病（Covid-19）的可能性是其他人的两倍。这些健康 在整个大流行病期间，无法充分获得基本资源加剧了差距。这种不平等不是 新冠肺炎的独特之处在过去的世纪里，新出现的传染病极大地延长了健康差距 in underserved欠发达communities社区.导致这些差异的相互关联的途径，包括异质性疾病 流行病学、社会人口特征以及治疗的获得和接受情况仍然没有得到充分的研究。移动的健康 通过及时提供干预措施， 医疗服务不足的人群。然而，由于无法有效地确定和优先处理高风险社区， 对MHC决策者提出了严峻的挑战，并导致了次优的分配策略。以帮助改善 为了提高这些实地干预措施的效率，并减少新冠肺炎和未来大流行期间的健康差距，我们 一项提案旨在开发一个建模工具包，以改善服务不足地区的传染病监测和预测。 （b）确保人口的安全，并优先向高风险社区真实的提供基本资源。我们的创新， 多级建模框架将利用统计模型，机器学习，基于分区和基于代理 减少健康差距的模式：1）建立传染病监测的实时数据系统 2）确定高危人群，以便分配 基本资源，3）评估社会人口统计学和临床特征之间的复杂相互作用，传染性 疾病流行病学，可改变的健康障碍和干预措施，以改善应急规划， 2019冠状病毒病大流行和未来的卫生紧急情况，以及4）建立一个建模工具包，为提供信息提供信息， 实时向服务不足的社区提供必要的资源。这将通过实时集成 传染病结果数据，人口统计学、社会经济学和临床特征，疫苗犹豫调查， 社区一级的背景因素，以及关于卫生保健的结构性障碍的数据，用于估计 动态仿真建模框架。我们提出的框架将推广到其他传染病 疾病，其中模型输入将取决于疾病和位置，以便迅速转化为其他公共卫生问题。 为了展示我们的工具包的实用性，我们的建模框架将重点关注Covid-19移动的疫苗接种的交付 在南卡罗来纳州（SC）的诊所服务不足的人群。我们的建议将通过制定 疾病监测的建模基础设施和对服务不足地区传染病流行病学的了解 社区，最终改善向最需要的人及时提供基本资源的情况。COVID-19声称 截至2022年2月，已有近100万美国人死亡，超过400万人住院治疗。利用这种 公共卫生决策者的工具包可以防止未来数千人死于新冠肺炎。通过调整投入 数据源，我们的建模框架很容易翻译到其他传染病和地理区域， 在未来的流行病中拯救更多生命的潜力。