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

基本信息

批准号：
10668373
负责人：
Nadia Natasha Abuelezam
金额：
$ 39万
依托单位：
BOSTON COLLEGE
依托单位国家：
美国
项目类别：
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/10668373
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 Goals Guidelines Health 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 Race Research Science Severities Social Environment Specificity Structural Models Structure System Testing United States Work cost disease disparity disease transmission experience flexibility geographic disparity in silico infectious disease model mathematical model novel pathogen policy recommendation prevent programs racial disparity residential segregation simulation social social determinants tool

项目摘要

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颖的建模方法、工具和指南，以将通过明确地将种族化的生活经验纳入传染病传播的数学模型中对传染病暴露、易感性和严重程度方面种族差异的结构性驱动因素进行建模，和后果。特别是，这项研究将有意涉及地理差异美国通过地理信息系统（GIS）编码数据来凸显其重要性整个生命过程中传染病传播的社会背景和决定因素。我们将利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。计算机模拟允许代表个人和家庭（及其特征）在特定地理区域内。我们计划修改模型结构来探索通过添加各种模型特征（包括随机性、自然性）获得的影响和特异性历史、环境影响。然后我们的目标是进行全面的敏感性分析考虑社会和政治背景，并纳入可能有助于的多种相互作用因素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历（代表并参数化为代理特征）。虽然建模框架很灵活，但我们将使用以下数据 SARS-CoV-2 和流感作为两个例子来证明我们开发的方法的可行性。这项工作的结果将使我们能够制定结构性干预措施的政策建议，减少传染病结果的种族差异。将结构性干预措施纳入模型结构需要灵活性来考虑对个体行为的干扰和反馈。我们计划在计算机模拟中检查使用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、减少收入不平等以及分配以美国各地的现实世界项目为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奠定基础防止有色人种社区承受不平等的健康和费用相关负担。