RAPID: Retrospective COVID-19 Scenario Projections Accounting for Population Heterogeneities

COVID-19的长期负担可能因种族和民族而异。为了解决这一差异，该项目将扩展现有模型，以考虑种族和民族。在美国，按人种/种族列出的结局和疫苗接种数据的可用性为明确建模各组中的这些变量并评估真实世界数据的结果创造了机会。该项目将帮助我们了解COVID-19结果和疫苗接种的不公平性，并为美国未来的COVID-19和其他疫情做好准备。该项目有可能适用于任何有关于族裔和种族的相关数据的地方，并可推广到其他类型的群体。该项目将整合在编程本科课程和机器学习健康研究生课程中学到的经验教训。该项目还将通过高级顶点计划和少数民族服务计划，如南加州大学启动计划和维特比夏季研究所提供研究机会。拟议的项目将整合种族和民族数据沿着与其他各种数据集，以说明人口健康。集成的关键创新是能够从数据中学习联系矩阵。该项目将使用一种新的方法，其中n×n接触矩阵由n个隐藏参数生成，这些参数表示一个群体与随机选择的个体接触的可能性。了解到的接触矩阵将与PI目前在美国情景建模中心使用的流行病学模型相结合，以生成病例、死亡和住院的长期预测。该方法将学习接触矩阵与其他方法进行比较，这些方法从调查数据和高分辨率移动数据中获得这些矩阵。这种新方法将能够在没有这种流动性数据的情况下对亚群体的相互作用进行建模。该模型将通过与COVID-19情景建模中心合作，使用过去三年观察到的地面实况数据进行评估。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The long-term burden of COVID-19 may vary across races and ethnicities. To address this variaiton this project will extend a current model to account for race and ethnicity. The availability of outcomes and vaccine uptake data by race/ethnicity in the US creates an opportunity to explicitly model these variables across the groups and evaluate the results from real-world data. The project will help us understand the inequities of COVID-19 outcomes and vaccination uptake and prepare the US for the future of COVID-19 and other outbreaks. The project has the potential to be applicable wherever relevant data on ethnicity and race is available, and can be extended to other types of groups. The project will integrate the lessons learned in an undergraduate course on programming and a graduate-level class on Machine Learning for health. The project will also provide research opportunities through a senior capstone program and minority-serving programs such as the USC JumpStart program and the Viterbi Summer Institute.The proposed project will integrate data on race and ethnicity along with various other datasets to account for population health. The key innovation in the integration is the ability to learn contact matrices from data. The project will use a novel approach, where the n×n contact matrix is generated by n hidden parameters that indicate the likelihood of contact of a group with a randomly selected individual. The learned contact matrix will be integrated with an epidemiological model currently being used by the PI in the US Scenario Modeling Hub to generate long-term projections of cases, deaths, and hospitalization. The appoach will compare learning contact matrices with other approaches that derive those matrices from survey data and high-resolution mobility data. The new approach will enable the modeling of sub-population interactions when such mobility data is not available. The model will be evaluated with ground truth data observed over the last three years in collaboration with the COVID-19 Scenario Modeling Hub.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.