RAPID: Fast COVID-19 Scenario Projections in Presence of Vaccines and Competing Variants

一年多过去了，新冠肺炎仍然是全球关注的焦点。虽然美国正朝着快速重新开放经济活动的方向前进，但关键是要确保能够在不增加医疗体系负担的情况下做到这一点。因此，当务之急是对病例、住院和死亡情况做出可靠的长期预测，以告知决策者。由于新出现的具有传播性优势的竞争变种、可能的免疫减弱和免疫逃逸、疫苗的犹豫不决以及非药物干预的变化，在建模和估计方面正在出现新的挑战。该项目将在美国州一级的情景预测中解决这些新出现的挑战。建模技术的一个关键优势是，它可以结合各种复杂性，并从不断变化的流行病学和社会环境中学习，但它可以快速做出预测。该项目开发的技术不仅适用于美国的地点，也适用于世界各地新冠肺炎仍然是一场严重灾难的地点。在该项目期间开发的情景建模框架还将为快速生成情景奠定基础，以便在未来流行病期间更好地做好准备。该项目为研究生提供了培训机会。建议的项目开发了一个离散时间异质速率模型，该模型可以纳入新冠肺炎的各种复杂性，同时还可以快速生成商品硬件的长期情景预测(美国所有州的2-3分钟/情景)。通过分离参数估计，可以快速预测病例、住院和死亡，以便可以使用简单的回归技术独立学习它们。这也消除了由于同时学习复杂的相互依赖的参数和高维机器学习方法而产生的过拟合。预测是作为给定情景、健康结果、周和地点的概率分位数生成的；分位数是基于由数据输入和估计中的不确定性产生的预测集合来预测的。该项目还将开发一种新的基于约束优化的学习方法，从基因组数据中估计竞争变体的时间动力学。该奖项反映了NSF的法定使命，并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。

After more than a year, COVID-19 remains a concern worldwide. While the United States is moving towards a fast reopening of economic activities, it is crucial to ensure that it can be done without an increased burden on the healthcare system. As a result, there is an urgency to produce reliable long-term scenario projections of cases, hospitalizations, and deaths to inform policymakers. New challenges in modeling and estimations are emerging due to emerging competing variants with transmissibility advantage, possible waning immunity and immune escape, vaccine hesitancy, and changes in non-pharmaceutical interventions. The project will address these emerging challenges in scenario projections at the state-level in the US. A key advantage of the modeling technique is that it can incorporate various complexities and learn from a changing epidemiological and social environment, and yet it can produce fast projections. The techniques developed in the project will not only be applicable to the US locations but also locations around the world where COVID-19 is still a severe disaster. The scenario modeling framework developed during the project will also set the foundations for quick scenario generation for better preparedness during future epidemics. This project provides training opportunities for a graduate student.The proposed project develops a discrete-time heterogeneous rate model that can incorporate various complexities of COVID-19 and yet produce long-term scenario projections quickly on commodity hardware (2-3 mins/scenario for all US states). The fast projections of cases, hospitalizations, and deaths are enabled by decoupling of the parameter estimations so that they can be learned independently using simple regression techniques. This also results in the elimination of over-fitting arising from simultaneously learning complex interdependent parameters and from high-dimensional machine learning approaches. Projections are generated as probabilistic quantiles for a given scenario, health outcome, week, and location; the quantiles are predicted based on an ensemble of projections resulting from the uncertainties in data inputs and estimations. The project will also develop a novel constrained optimization-based learning approach to estimate the temporal dynamics of competing variants from genomics data.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.