Accelerating Excellence in Translational Science (AXIS)

摘要 该项目将使用国家和地方的发病率、死亡率和检测数据，并由地理信息系统驱动 基于代理人的疾病建模，以确定导致不均衡的“邻里效应” 新冠肺炎的传播模式和严重后果。邻里环境 与慢性病死亡率、残疾、累积压力、认知能力 衰退，身体机能丧失。这些相同的邻里效应隐含在 加剧了新冠肺炎传播和健康结果方面的差异，然而，确切的 根深蒂固的社会差距产生影响的机制和程度 新冠肺炎的结果尚不清楚。关键假设是基于病原体的疾病传播 对国家和地方两级现有的追溯新冠肺炎数据源进行建模 通过地理空间数据输入和时空分析，将提供有关 影响大流行在当地传播的结构性因素，并将促进 就如何缩小当前和未来的差距提出有价值的建议 新冠肺炎和其他未来传染病和流行病的影响。我们将测试我们的密钥 假设并通过以下具体目标实现我们的目标。 确定与健康的社会决定因素相关的县因素影响了哪些县 冠状病毒在美国的传播。 1)确定在所确定的县内，哪些社会决定因素对 对传播或抑制CV-19传播的影响。 2)确定哪些人口群体在这些地方受到的影响更严重 背景和原因？ 3)确定根据这些调查结果可以提出哪些具体建议？ 这项研究的预期结果是发表手稿和学术报告 为卫生当局和政策制定者提供基于科学的建议 能够主动应对在某些情况下放大疾病和不良健康结局的社会因素 社区。

Abstract This project will use national and local incidence, mortality and testing data, with GIS driven agent-based disease modelling to identify the “neighborhood effects” that drive the uneven patterns of transmission and serious outcomes from COVID-19. Neighborhood environments have been associated with chronic disease mortality, disability, cumulative stress, cognitive decline, loss of physical functioning. These same neighborhood effects are implicit in exacerbating disparities in the spread and health outcomes of COVID-19, however the exact mechanisms and the magnitude of the impact of entrenched social disparities specifically on COVID-19 outcomes are not yet known. The key hypothesis is that agent based disease spread modeling of the existing retrospective COVID-19 data sources at the national and local levels with geospatial data input and spatial-temporal analysis will provide powerful knowledge on the structural factors that influenced the pandemic’s spread in local areas and will facilitate the development of valuable recommendations on how to mitigate current and future disparities in impacts of COVID-19 and other future infectious epidemics and pandemics. We will test our key hypothesis and accomplish our objectives via the following Specific Aims. Determine what Counties factors related to the social determinants of health have influenced the spread of coronavirus in the United States. 1) Determine, within the identified Counties, which social determinants have had significant impact on either spread or inhibit spread of CV-19. 2) Determine which populations groups have been impacted more severely in these local contexts and why? 3) Determine what specific recommendations can be made from these findings? The expected outcomes of this research are published manuscripts and scholarly presentations that provide science- based recommendations on the how health authorities and policy makers can proactively address social factors that amplify disease and poor health outcomes in certain communities.