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

基本信息

  • 批准号:
    10584876
  • 负责人:
  • 金额:
    $ 68.12万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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.
项目概要 黑人、西班牙裔和农村美国人死于 2019 年冠状病毒病 (Covid-19) 的可能性是其两倍。这些健康 整个大流行期间,基本资源获取不足加剧了不平等。这种不平等现象并非 Covid-19 独有的。在过去的一个世纪里,新出现的传染病极大地延续了健康差距 在服务不足的社区。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、社会人口学特征以及治疗的获取和接受情况仍未得到充分研究。移动健康 诊所 (MHC) 是一种有效且多功能的工具,可通过及时向患者提供干预措施来减少健康差距 医疗服务不足的人群。然而,由于无法有效识别高风险社区并确定其优先顺序, 给 MHC 决策者带来了严峻的挑战,并导致了次优的分配策略。为了帮助改善 这些实地干预措施的效率并减少 Covid-19 和未来大流行期间的健康差距,我们 该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 并优先考虑向高风险社区实时提供基本资源。我们的创新、 多级建模框架将利用统计模型、机器学习、基于隔室和基于代理的 通过以下方式减少健康差异的模型: 1) 建立传染病监测实时数据系统 以及对服务不足社区的疾病流行病学进行估计 2) 确定高危人群以分配 重要资源,3)评估社会人口统计学和临床​​特征、传染性之间复杂的相互作用 疾病流行病学、可改变的健康障碍和干预措施的采用,以改进应急计划 Covid-19 大流行和未来的突发卫生事件,以及 4) 建立建模工具包以通知交付 实时为服务不足的社区提供重要资源。这将通过实时集成来完成 传染病结果数据、人口、社会经济和临床特征、疫苗犹豫调查、 社区层面的背景因素以及卫生保健结构性障碍的数据,用于估计关键输入参数 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地点,以便迅速转化为其他公共卫生问题。 为了展示我们工具包的实用性,我们的建模框架将重点关注 Covid-19 移动疫苗接种的交付 为南卡罗来纳州 (SC) 服务不足的人群提供诊所。我们的建议将通过制定 用于疾病监测和了解服务不足地区传染病流行病学的建模基础设施 社区,最终改善向最需要的人及时提供必要资源的情况。 Covid-19 已声称 截至 2022 年 2 月,已有近 100 万美国人丧生,超过 400 万人住院治疗。 公共卫生决策者开发的工具包可以预防未来数千人因 Covid-19 死亡。通过输入的适应 数据源,我们的建模框架很容易转化为其他传染病和地理区域,并且具有 在未来的流行病中拯救更多生命的潜力。

项目成果

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