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

项目摘要 工作不安全和疾病风险密不可分，SARS-CoV-2大流行凸显了 这两个关键结果之间的相互依存关系。在疾病传播方面， 疾病传播依赖于大规模行动易感-（暴露）-感染-移除（SIR， SEIR）框架或调整到SIR动态的曲线拟合模型。这些模型中最好的使用隔间 被认为是生物相关的，如年龄，但他们通常不包括社会相关性，有效地 忽视了众所周知的隔离和社会分层的障碍，互动可能渠道感染。我们 迫切需要能够准确说明核心人群在疾病风险和负担方面差异的模型。 社区的种族和民族隔离、生活方式的差异 安排，以及避免与他人密切接触的能力，这是众所周知的 健康差距，并导致更差的COVID-19结果。通过假设这些特征，我们错过了如何 疾病和疾病预防活动的负担在弱势群体之间的分担不均衡。上 在经济负担方面，众所周知，工作不安全是由种族和社会经济地位造成的， 美国的非裔美国人和拉丁美洲人比白人更有可能从事小时工资的工作， 不稳定的工作，因此，这些人口特别容易失去工作，收入减少， 和福利，以及经济紧缩期间其他与工作有关的削减。同样，有标记的 梯度沿着财富分配的经济脆弱性造成的赤字，在储蓄需要， 在收入减少或损失期间支付基本生活费用。重要的是， 经济脆弱的人感染COVID-19等疾病的风险也更高。非洲 美国人，拉丁美洲人和其他低社会经济地位人群患病的风险特别高， 住院，死于COVID-19引起的并发症。重要的是，工作导致的行为 不安全可能加剧疾病风险;疾病可能加剧工作不安全。大多数尝试 对这些过程进行建模并没有考虑到这种基本的相互依赖性。我们建议建立和 测试疾病传播和社会经济结果的完全集成的基于代理的模型（ABM）。在目标1中， 我们将根据真实的社会网络和活动数据建立反弹道导弹系统，这些数据反映了强关系、弱关系和非强关系的混合。 关系，以及偶然的个人接触。在目标2中，我们将ABM拟合到观察到的流行模式，以确定关键的 驱动特性。在目标3中，我们提出了有助于确定内在权衡的政策替代方案 公共安全和经济困难之间的关系，以及这些结果如何不平等地分布在 在这个国家工作的人。

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.