Systems modeling to address the social and biological drivers of disparities in infection and mortality from emerging infectious diseases

用于解决新发传染病感染和死亡率差异的社会和生物驱动因素的系统建模

• 批准号: 10415713
• 负责人: Jonathan L Zelner
• 金额: $ 67.93万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-20 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10415713
• 关键词: 2019-nCoV; Address; Attitude; Automobile Driving; Base Ratios; Behavior; Behavior Therapy; Behavioral; Biological; Biological Models; COVID-19; COVID-19 mortality; COVID-19 pandemic; COVID-19 vaccination; Childhood; Clinical Data; Clinical effectiveness; Communicable Diseases; Data; Data Analyses; Data Collection; Dimensions; Disease; Disease Outbreaks; Disease Progression; Dose; Effectiveness; Emerging Communicable Diseases; Ensure; Epidemic; Epidemiologic Factors; Epidemiology; Ethnic Origin; Etiology; Evaluation; Foundations; Frequencies; Future; Geography; Goals; Herd Immunity; Immunity; Incidence; Individual; Inequality; Infection; Intervention; Intervention Studies; Joints; Limb structure; Long-Term Effects; Longitudinal cohort; Masks; Measles; Measures; Medical Sociology; Methods; Minority Groups; Mission; Modeling; Morbidity - disease rate; National Institute on Minority Health and Health Disparities; Nature; Neighborhoods; Observational Study; Outcome; Phase; Policies; Population; Population Surveillance; Predisposition; Prospective Studies; Public Health; Race; Readiness; Research; Research Design; Risk; Series; Socioeconomic Status; Sociology; Statistical Models; Surveys; Thinking; Time; Vaccination; Vaccines; Work; Yawning; design; disease transmission; disparity reduction; dynamic system; effectiveness evaluation; emerging pathogen; foot; geographic disparity; health care availability; infection risk; infectious disease model; intervention effect; lens; low socioeconomic status; models and simulation; mortality; mortality risk; pandemic disease; pathogen; population based; prevent; prospective; protective effect; racial disparity; racial minority; residential segregation; response; simulation; social; social contact; social determinants; social epidemiology; social factors; social inequality; socioeconomic disparity; socioeconomics; surveillance data; surveillance study; therapy design; tool; transmission process; vaccine access; vaccine hesitancy

Project Summary/Abstract The distribution of disease and death from the COVID-19 pandemic has been grossly unequal in every dimension. The vaccination campaign, throughout Winter and Spring 2021, has seen these inequities repeated. Lower-risk, wealthier, and Whiter individuals have received earlier access to vaccination than their counterparts. To those viewing the pandemic through the theoretical lens of social epidemiology and medical sociology, the extremity and nature of these disparities was easily anticipated. However, the predictive and dynamic systems models that have guided the domestic and global COVID-19 response have routinely ignored the social determinants of infection and its outcomes. The objective of this application is to outline a multi-level approach to infectious disease transmission modeling and data analysis that places the social determinants of exposure, severe disease and mortality on an equal footing with the biological features of transmission and disease progression. Our overarching goal is to develop a set of tools that will extend lessons from the COVID-19 pandemic to prevent similar disparities in future outbreaks, epidemics, and pandemics. The first aim of this project will develop and analyze transmission models that integrate the joint social and biological drivers of infection disparities. This proposed work will identify etiologic factors driving disparities in infection risk, and propose policy-relevant alternative approaches to measuring infection disparities. Our second aim will evaluate the sensitivity of population-based prospective and observational study designs to socioeconomic disparities in infection risk and outcomes. We will use simulation studies with input parameters derived from the analysis of detailed SARS-CoV-2 case data to understand the circumstances under which these study designs obscure key dimensions of disparity. The third aim will assess long-term effects of vaccination policies, behavior, and interventions on population-level infection inequalities. As the COVID-19 vaccination campaign has progressed it has become clear that vaccine hesitancy and vaccine access are dual threats to achieving substantial levels of population immunity. We will integrate survey data on vaccine hesitancy with data on healthcare access and SARS-CoV-2 incidence to parameterize a spatial transmission model highlighting inequity in risks and avenues for closing these gaps for COVID-19 and other vaccine preventable diseases. Taken together, the proposed projects will lay the foundation systems modeling tools that can be used to promote equity in future epidemic and pandemic responses.

项目总结/摘要 2019冠状病毒病大流行的疾病和死亡分布在每个国家都非常不平等， 维度在整个2021年冬季和春季的疫苗接种活动中，这些不公平现象一再出现。 较低风险、较富裕和较白的人比他们的同行更早获得疫苗接种。 对于那些通过社会流行病学和医学社会学的理论透镜来看待这一流行病的人来说， 这些差异的极端性和性质是很容易预料到的。然而，预测和动态系统 指导国内和全球COVID-19应对的模型通常忽略了社会因素， 感染及其后果的决定因素。本申请的目的是概述一个多层次的方法 传染病传播建模和数据分析，将暴露的社会决定因素， 严重的疾病和死亡率与生物传播和疾病的特征平等 进展我们的总体目标是开发一套工具，以扩展COVID-19的经验教训 大流行，以防止在未来的爆发，流行病和大流行病类似的差距。第一个目标是 该项目将开发和分析传播模型，整合联合社会和生物驱动因素， 感染差异。这项拟议的工作将确定导致感染风险差异的病因因素， 提出与政策相关的替代方法，以衡量感染差异。我们的第二个目标是评估 基于人群的前瞻性和观察性研究设计对社会经济差异的敏感性， 感染风险和结果。我们将使用模拟研究，输入参数来自对 详细的SARS-CoV-2病例数据，以了解这些研究设计模糊关键的情况。 差距的维度。第三个目标是评估疫苗接种政策、行为和 对人口感染不平等采取干预措施。随着COVID-19疫苗接种活动的进展， 很明显，疫苗犹豫和疫苗获得是实现实质性水平的双重威胁， 人口免疫力。我们将把关于疫苗犹豫的调查数据与关于医疗保健获取的数据结合起来， SARS-CoV-2发病率与相关疾病的关系--一个强调风险和途径不平等的空间传播模型 用于缩小COVID-19和其他疫苗可预防疾病的差距。综合考虑， 项目将奠定基础的系统建模工具，可用于促进公平，在未来的流行病 和流行病应对措施。