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

新冠肺炎的长期负担可能因种族和民族而异。为了解决这一变化，该项目将扩展当前的模型，以考虑种族和民族。在美国，按种族/民族提供的结果和疫苗接种率数据创造了一个机会，可以明确地对这些群体中的这些变量进行建模，并从现实世界的数据中评估结果。该项目将帮助我们了解新冠肺炎结果和疫苗接种情况的不平等，并为美国应对新冠肺炎和其他疫情的未来做好准备。只要有关于族裔和种族的相关数据，该项目就有可能适用，并可推广到其他类型的群体。该项目将整合从本科编程课程和研究生级别的健康机器学习课程中学到的经验教训。该项目还将通过高级顶峰计划和少数群体服务计划提供研究机会，如南加州大学JumpStart计划和维特比夏季学院。拟议的项目将整合种族和民族数据以及各种其他数据集，以考虑人口健康。集成中的关键创新是能够从数据中学习联系人矩阵。该项目将使用一种新的方法，其中n×n联系矩阵由n个隐藏参数生成，这些参数表明一组人与随机选择的个人接触的可能性。学习到的联系人矩阵将与美国情景建模中心PI目前使用的流行病学模型相结合，以生成对病例、死亡和住院的长期预测。这项研究将把学习联系矩阵与从调查数据和高分辨率流动数据中得出这些矩阵的其他方法进行比较。新方法将能够在没有这样的流动性数据时对子种群相互作用进行建模。该模型将根据过去三年与新冠肺炎场景建模中心合作观察到的地面真实数据进行评估。该奖项反映了美国国家科学基金会的法定使命，并已通过使用基金会的智力优势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.