Economic security and health disparity in COVID-19: A computational modeling approach.

项目总结 工作不安全和疾病风险密不可分，SARS-CoV-2大流行突显了 这两个关键结果之间的相互依存关系。在疾病传播方面，目前的模型 疾病传播依赖于易感(暴露)-感染-移除的群体性行为的变种(先生， SEIR)框架或曲线拟合模型调整到SIR动态。这些车型中最好的是使用隔间 被认为是生物学上相关的，例如年龄，但它们通常不包括有效的社会相关性 忽视了众所周知的隔离和社会分层障碍，这可能会导致渠道感染。我们 迫切需要能够准确反映核心人群在疾病风险和负担方面差异的模型。 疾病暴露在很大程度上是由社区的种族和民族隔离、生活方式的差异 安排，以及避免与他人进行密切个人接触的能力，这些都与众所周知的 健康差距和导致更差的新冠肺炎成果。通过假设这些特征，我们错过了 疾病和疾病规避活动的负担在易受伤害人群中分担的情况并不均衡。论 在经济负担方面，众所周知，工作不安全感是由种族和社会经济地位决定的 美国。非洲裔美国人和拉美裔美国人比白人更有可能以小时工资工作， 工作不稳定，因此，这些人口特别容易受到失业、收入减少的影响 和福利，以及在经济紧缩期间与工作相关的其他削减。同样，也有标记为 所需储蓄赤字导致的经济脆弱性沿财富分配的梯度 支付收入减少或损失期间的基本生活费用。重要的是，同样的人口 经济上脆弱的人感染新冠肺炎等疾病的风险也更高。非洲 美国人、拉丁裔和其他低SES人群患病的风险特别高， 住院，死于新冠肺炎引起的并发症。重要的是，工作导致的行为 不安全感可能会加剧疾病风险；疾病可能会加剧工作不安全感。尝试次数最多 在对这些过程进行建模时，没有考虑到这种基本的相互依赖关系。我们建议建造和 测试疾病传播和社会经济结果的完全集成的基于主体的模型(ABM)。在目标1中， 我们将基于真实的社交网络和活动数据构建ABM，这些数据反映了强关系、弱关系和弱关系的组合 领带，以及附带的个人接触。在目标2中，我们将ABM与观察到的流行模式进行匹配，以确定关键 差异驱动功能。在目标3中，我们提出了能够帮助确定内在权衡的策略替代方案 公共安全和经济困难之间的关系以及这些结果是如何在 这个国家的劳动人民。

Project summary Job insecurity and disease risk are inextricably linked, and the SARS-CoV-2 pandemic has highlighted the interdependence between these two critical outcomes. On the disease transmission side, current models for disease transmission rest on variants of mass-action Susceptible – (Exposed) – Infected – Removed (SIR, SEIR) frameworks or curve-fitting models tuned to SIR dynamics. The best of these models use compartments that are deemed biologically relevant, such as age, but they typically do not include social relevance, effectively ignoring well-known segregation and social stratification barriers to interaction that likely channel infection. We urgently need models that accurately account for core population differences in risk and burden of disease. Disease exposure is deeply structured by the racial and ethnic segregation of communities, differences in living arrangements, and ability to avoid close personal contact with others, which are compounded by well-known health disparities and lead to poorer COVID-19 outcomes. By assuming away such features, we miss how unevenly the burden of disease and disease avoidance activity is shared across vulnerable populations. On the economic burden side, it is well-known that job insecurity is patterned by race and socioeconomic status in the United States. African Americans and Latinos are considerably more likely than whites to work in hourly-wage, precarious jobs, and as a result, these populations are particularly vulnerable to job loss, reductions in income and benefits, and other job-related cutbacks during economic retrenchments. Similarly, there are marked gradients along the wealth distribution in economic vulnerability resulting from deficits in savings needed to cover basic living expenses during periods of income reduction or loss. Importantly, the very same populations who are economically vulnerable are also at higher risk of contracting diseases like COVID-19. African Americans, Latinos, and other low-SES populations are at particularly high risk of becoming ill, being hospitalized, and dying of complications resulting from COVID-19. Importantly, behaviors resulting from job insecurity are likely to exacerbate disease risk; and disease is likely to exacerbate job insecurity. Most attempts to model these processes do not take this essential interdependence into account. We propose to build and test a fully integrated Agent Based Model (ABM) of disease spread and socio-economic outcomes. In Aim 1, we will build the ABM based on real social network and activity data that reflect the mix of strong ties, weak ties, and incidental personal contacts. In Aim 2, we fit the ABMs to observed epidemic patterns to identify key disparity-driving features. In Aim 3, we propose policy alternatives that can help identify inherent tradeoffs between public safety and economic hardship and how such outcomes are unequally distributed across working people in the country.