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

项目总结 不同社会人口群体在疾病动态和健康结果方面的显著差异 在SARS-CoV-2大流行期间观察到的数据突显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都会产生 在空间尺度上的洞察力过于粗略(例如，县、州)，并且通常对当地不敏感 疾病动态中的社会人口学差异；这限制了它们为公共卫生提供信息的实用价值 在当地社区进行规划。需要的是一套标准化的方法来有效地创建精细的- 能够揭示和捕捉关键空间要素和社会人口因素的粒化模型 影响传播动态和疾病结果，这可以揭示不同的社会人口 亚组可能会经历不同的疾病结果。在这里，我们提出了一种新的SSCIMA(Social-Space 聚类、互联和运动分析)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，并优化 构建元人口疾病模型，在规模上做出更准确的疾病预测 人口普查区块(即当地社区)。我们将使用SARS-CoV-2和模拟数据集来驱动 设计和严格测试对未来大流行有用的通用方法和软件 做好准备。在目标1中，我们开发了统计方法，以获取疾病数据、流动模式和社会- 人口普查区块规模的人口统计数据，并使用这些数据来确定最 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揭示的联系来 使元人口模型适应人口普查区块规模的稀疏数据、集成移动性数据和 基于进化直接构建高保真元种群模型的人口统计特征 观察到的疾病动态模式。由模拟和真实数据驱动的分析将揭示 SSCIMA驱动的元人口模型配置，以提高本地预测精度；我们还将 制作免费提供的软件和基于云的建模门户，以允许对我们的 方法和工具。在目标3中，我们将以传播和教育为重点，开发一种新的教育 在SHERC现有的外联基础设施内部署的模块，以及为期半天的培训 为建模社区举办研讨会，以了解、参与并提供有关我们的技术和 工具。我们希望SCCIMA方法能够实现更快速、空间更精细和以股票为重点的建模 这些努力将使我们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.