Advancing Methods in Infectious Diseases Models: Incorporating Structural Causes

项目摘要 这项研究计划旨在开发新的建模方法、工具和指南，以纳入 通过显式地将生活经验引入传染病传播的数学模型 模拟传染病暴露、易感性和严重性方面种族差异的结构性驱动因素， 以及后果。特别是，这项研究将有意识地涉及到 美国通过地理信息系统(GIS)编码数据来突出重要性 社会背景和整个生命过程中的决定因素对传染病的传播。 我们将利用系统科学在电子仿真中进行分析，并对仿真的后期数据进行分析 产出，以了解传染病差异的结构性驱动因素。在硅胶模拟中允许 代表个人和家庭(及其家庭)的合成种群的发展 特征)在特定地理区域内。我们计划修改模型结构，以探索 通过添加各种模型特征获得的影响和特异性，包括随机性、自然 历史和环境的影响。然后，我们的目标是进行全面的敏感性分析 考虑到社会和政治背景，并纳入可能有助于 确定特定疾病类型的传播模式。最终，In Silico模拟的目标是 通过种族主义的生活经验(代表)，在数学上将政策效果与健康结果联系起来 并被参数化为代理特征)。虽然建模框架将是灵活的，但我们将在 以SARS-CoV-2和流感为例，验证了我们所开发的方法的可行性。 这项工作的结果将使我们能够为结构性干预制定政策建议，以 减少传染病结果中的种族差异。将结构性干预措施纳入 模型结构将需要灵活性，以考虑个人行为的干扰和反馈。 我们计划在计算机模拟中使用的结构性干预措施包括消除住宅 种族隔离，增加获得稳定住房的机会，减少收入不平等，以及分配 全美各地的现实世界项目所代表的健康食品选择。这项研究将奠定 为持续控制现有和新出现的传染病病原体和 防止有色人种社区在健康和成本方面的不平等负担。

PROJECT ABSTRACT This research program aims to develop novel modeling methods, tools, and guidelines to incorporate racialized lived experiences into mathematical models of infectious disease transmission by explicitly modeling structural drivers of racial disparities in infectious disease exposure, susceptibility and severity, and consequences. In particular, this research will intentionally engage with geographic disparities in the United States through geographic information systems (GIS) coded data to highlight the importance of social context and determinants across the life course to the transmission of infectious diseases. We will employ systems science to analyze in silico simulations and post-hoc data analysis of simulation output to understand the structural drivers of infectious disease disparities. In silico simulation allows for the development of synthetic populations that represent individuals and households (and their characteristics) within a particular geographic area. We plan to modify the model structure to explore the impact and specificity gained by adding a variety of model characteristics, including stochasticity, natural history, and environmental influence. We then aim to perform comprehensive sensitivity analyses accounting for social and political context and the incorporation of multiple interacting factors that may help identify patterns in spread of particular disease types. Ultimately, the goal of the in silico simulations is to mathematically link policy effects to health outcomes through racialized lived experiences (represented and parameterized as agent characteristics). While the modeling frame will be flexible, we will use data on SARS-CoV-2 and influenza as two examples to demonstrate the feasibility of the methods we develop. The results from this work will allow us to develop policy recommendations for structural interventions to reduce racial disparities in infectious disease outcomes. Incorporating structural interventions into the model structure will require flexibility to account for the interference and feedback with individual behaviors. The structural interventions we plan to examine using in silico simulations include eliminating residential segregation, increasing accessibility to stable housing, reducing income inequality, and distribution of healthy food choices represented by real-world programs across the United States. This research will lay the groundwork to inform ongoing control of existing and emerging infectious disease pathogens and prevent the unequal health- and cost-related burdens on communities of color.