Advancing Methods in Infectious Diseases Models: Incorporating Structural Causes

传染病模型的改进方法：纳入结构性原因

• 批准号: 10275801
• 负责人: Nadia Natasha Abuelezam
• 金额: $ 39万
• 依托单位: BOSTON COLLEGE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10275801
• 关键词: 2019-nCoV; Accounting; Behavior; Characteristics; Code; Color; Communicable Diseases; Communities; Data; Data Analyses; Development; Disease; Disease Outcome; Emerging Communicable Diseases; Feedback; Geographic Information Systems; Geographic Locations; Geography; Goals; Guidelines; Health; Health Care Costs; Health Food; Household; Housing; Income; Individual; Inequality; Influenza; Intervention; Life Cycle Stages; Link; Mathematics; Methods; Modeling; Natural History; Outcome; Output; Pattern; Policies; Politics; Population; Predisposition; Recommendation; Research; Science; Severities; Social Environment; Specificity; Structural Models; Structure; System; Testing; United States; Work; disease disparity; disease transmission; experience; flexibility; in silico; infectious disease model; mathematical model; novel; pathogen; prevent; programs; racial disparity; residential segregation; simulation; social; social determinants; tool

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.

项目摘要 该研究计划旨在开发新的建模方法，工具和指导方针， 将种族化的生活经验转化为传染病传播的数学模型， 对传染病暴露、易感性和严重性方面种族差异的结构性驱动因素进行建模， 和后果特别是，这项研究将有意参与地理差异， 美国通过地理信息系统（GIS）编码数据来突出其重要性 社会背景和整个生命过程中的决定因素，传染病的传播。 我们将采用系统科学来分析计算机模拟和模拟的事后数据分析 产出，以了解传染病差异的结构性驱动因素。计算机模拟允许 发展代表个人和家庭（及其 在特定的地理区域内。我们计划修改模型结构， 通过增加各种模型特征，包括随机性、自然 历史和环境影响。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，计算机模拟的目标是 通过种族化的生活经验（如 并被参数化为代理特性）。虽然建模框架将是灵活的，但我们将使用 以SARS-CoV-2和流感病毒为例，验证了本文方法的可行性。 这项工作的结果将使我们能够制定结构性干预的政策建议， 减少传染病结果的种族差异。将结构性干预纳入 模型结构将需要灵活性，以考虑对个体行为的干扰和反馈。 我们计划使用计算机模拟来检查的结构干预措施包括消除住宅 隔离，增加获得稳定住房的机会，减少收入不平等， 美国各地的真实世界项目所代表的健康食品选择。这项研究将奠定 为现有和新出现的传染病病原体的持续控制提供信息的基础， 防止对有色人种社区造成不平等的健康和成本负担。