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

一年多过去了，COVID-19仍是全球关注的问题。虽然美国正朝着快速重新开放经济活动的方向迈进，但至关重要的是要确保在不增加医疗保健系统负担的情况下实现这一目标。因此，迫切需要对病例、住院和死亡情况做出可靠的长期情景预测，以便为决策者提供信息。由于新出现的具有传播优势的竞争性变异、可能的免疫力下降和免疫逃逸、疫苗犹豫以及非药物干预措施的变化，正在出现建模和估计方面的新挑战。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，但它可以产生快速的预测。该项目开发的技术不仅适用于美国地区，也适用于全球COVID-19仍然是严重灾难的地区。项目期间制定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。这个项目为研究生提供了训练的机会。拟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COVID-19的各种复杂性，但可以在商品硬件上快速生成长期情景预测（2-3分钟/美国所有州的情景）。病例、住院和死亡的快速预测是通过参数估计的解耦实现的，因此它们可以使用简单的回归技术独立学习。这也消除了由于同时学习复杂的相互依赖参数和高维机器学习方法而产生的过度拟合。对于给定的场景、健康结果、星期和地点，以概率分位数的形式生成预测；分位数是根据数据输入和估计中的不确定性所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，以估计基因组学数据中竞争变体的时间动态。该奖项反映了美国国家科学基金会的法定使命，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。

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.