SSCIMA: Integrating Analysis of Socio-economic Sub-population Dynamics to Improve Spatial Models of Infectious Disease

项目摘要 不同社会人口群体之间疾病动态和健康结果的显著差异 在SARS-CoV-2大流行期间观察到的这些变化突出了改进建模的迫切需要 帮助我们理解和预测这种差异的方法。目前大多数建模研究产生 太粗糙的空间尺度上的洞察（例如，县，州）的规模，通常是不敏感的地方 疾病动态中的社会人口统计学差异;这限制了它们在告知公共卫生方面的实际价值 当地社区的规划。我们需要的是一套标准化的方法来有效地创造良好的- 能够揭示和捕获关键空间特征和社会人口因素的粒度模型， 影响传播动态和疾病结果，这可以揭示不同的社会人口 亚组可能经历不同的疾病结果。在这里，我们提出了一个新的SSCIMA（社会空间 聚类、互连和移动分析）建模方法，以有效地揭示链接 在当地流动性、社会人口构成和不断发展的疾病监测之间进行协调， 构建元人群疾病模型，可以在规模上做出更准确的疾病预测 人口普查区块（即，邻里）。我们将使用SARS-CoV-2和模拟数据集来驱动 设计和严格测试通用的方法和软件，这将有助于未来的大流行病 做好准备在目标1中，我们开发了吸收疾病数据、流动模式和社会影响的统计方法。 人口统计数据，并使用这些数据来确定最重要的特征， 强有力地解释了当地和区域传播动态以及疾病结果的模式（例如， 住院率）。在目标2中，我们开发了有效的方法，利用目标1中揭示的联系， 将元人口模型与人口普查区块规模的稀疏数据相匹配，整合流动性数据和社会 人口统计特征，以产生高保真元人口模型， 观察疾病动态的模式。由模拟和真实的数据驱动的分析将揭示 SSCIMA驱动的元人口模型配置，以提高本地预测的准确性;我们还将 开发免费软件和基于云的建模门户，以探索和测试我们的 方法和工具。在目标3中，我们将侧重于传播和教育，发展一种新的教育 在SHERC现有的外展基础设施中部署的模块，以及为期半天的培训 研讨会的建模社区学习，参与，并提供反馈，我们的技术和 工具.我们希望SCCIMA方法能够实现更快速、空间上更精确和以公平为中心的建模 这些努力将使我们能够更好地应对未来的流行病事件。

PROJECT SUMMARY The striking disparities in disease dynamics and health outcomes between various socio-demographic groups observed during the SARS-CoV-2 pandemic have highlighted the critical need for improved modeling approaches that help us understand and predict such disparities. Most modeling studies currently produce insights at spatial scales that are too coarse (e.g., counties, states) in scale, and are usually insensitive to local socio-demographic variations in disease dynamics; this limits their practical value for informing public health planning in local communities. What is needed is a set of standardized methods to efficiently create fine- grained models capable of exposing and capturing key spatial features and socio-demographic factors that impact transmission dynamics and disease outcomes, and that can reveal how different socio-demographic sub-groups may experience disparate disease outcomes. Here we propose a novel SSCIMA (Social-Spatial Clustering, Interconnection, and Movement Analysis) modeling approach to efficiently expose linkages between local mobility, socio-demographic composition, and evolving disease surveillance and to optimize the construction of meta-population disease models that can make more accurate disease forecasts at the scales of census blocks (i.e., local neighborhoods). We will use SARS-CoV-2 and simulated data sets to drive the design and rigorous testing of generalized methods and software that will be useful for future pandemic preparedness. In Aim 1, we develop statistical methods that ingest disease data, mobility patterns, and socio- demographic statistics at the scale of census blocks and use these data to determine the features that most strongly explain patterns of local and regional transmission dynamics as well as disease outcomes (e.g., hospitalization rates). In Aim 2, we develop efficient methods leveraging the linkages revealed under Aim 1 to fit meta-population models to sparse data at the scale of census blocks, integrating mobility data, and socio- demographic features to yield high-fidelity meta-population models structured directly based on evolving observed patterns of disease dynamics. Analyses driven by simulated and real data will reveal the potential for SSCIMA-driven configuration of meta-population models to improve local forecast accuracy; and we will also produce freely available software and a cloud-based modeling portal to allow exploration and testing of our method and tools. In Aim 3, we will focus on dissemination and education, developing a new educational module for deployment within SHERC’s existing outreach infrastructure, as well as a half-day training workshop for the modeling community to learn about, engage with, and provide feedback on our technique and tools. We expect the SCCIMA approach to enable more rapid, spatially refined, and equity-focused modeling efforts that will better equip us for future epidemic events.