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，工具和准则，以合并种族化的生活经验通过明确的传染病传播的数学模型在传染病暴露，易感性和严重程度中建模种族差异的结构驱动因素，和后果。特别是，这项研究将有意地与地理差异互动美国通过地理信息系统（GIS）编码数据，以突出显示的重要性在整个生命过程中，社会环境和决定因素传播传染性疾病。我们将采用系统科学来分析计算机模拟和仿真事后数据分析输出以了解传染病差异的结构驱动因素。在计算机模拟中允许代表个人和家庭的合成人群的发展（及其特定地理区域内的特征）。我们计划修改模型结构以探索通过添加各种模型特征（包括随机性，自然）获得的影响和特异性历史和环境影响。然后，我们旨在进行全面的灵敏度分析考虑社会和政治背景以及纳入多种相互作用的因素，可能有助于确定特定疾病类型传播的模式。最终，计算机模拟中的目标是数学上通过种族化的生活经验将政策效应与健康结果联系起来（代表并参数为代理特征）。虽然建模框架将是灵活的，但我们将使用数据 SARS-COV-2和流感作为两个例子，以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为结构干预措施制定政策建议减少传染病结果中的种族差异。将结构干预融合到模型结构将需要灵活性，以说明对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干预措施包括消除住宅种族隔离，增加对稳定住房的可及性，减少收入不平等以及分配美国各地的现实计划代表的健康食品选择。这项研究将进行为持续控制现有和新兴传染病病原体的基础防止有色社区的不平等健康和成本相关的负担。