RAPID: Modeling Outbreak of COVID-19 Using Dynamic Survival Analysis

2019冠状病毒病的爆发对预测区域疫情的动态和规模产生了巨大的需求。同样重要的是，需要确定早期干预措施（如关闭学校和强制或自我隔离）的潜在影响。为了回答这些问题，该项目将开发一个通用数学框架，用于仅使用部分观察到的每日新感染数（也称为流行曲线）的数据来分析正在发生的疫情趋势。PI的新框架将不假设任何特定的传染期或恢复期（通常是未知的）或可观察到的疾病流行情况。作为该项目一部分开发的工具将有助于预测新感染的增长率，并估计保持社交距离和其他预防措施对使流行病曲线趋于平缓的影响。PI将使用一种新的动态生存分析方法来预测美国中西部地区COVID-19流行的轨迹。来自世界其他地方的数据，如中国武汉市的数据，将被用来校准预测。该项目还将为一名博士生和一名博士后提供跨学科的实践培训。要开发的建模和预测框架与基于分区SIR模型中发病率或患病率计数的传统方法有根本不同。具体而言，PI将应用动态生存分析（DSA）方法，该方法考虑潜在大型随机网络的聚合平均场方程，并将其视为感染时间的近似生存律。PI将使用这些基于dsa的方程来模拟流行病和恢复曲线，并将其与COVID-19爆发期间观察到的曲线进行比较。在新框架的帮助下对流行病数据进行的统计分析将能够迅速阐明流行病的动态（例如，基本繁殖数R0）和干预措施的潜在影响（例如隔离或保持社会距离）。新框架将有助于更好地了解预防行为如何通过网络结构的变化和网络边缘疾病传播的变化影响COVID-19动态。该项目将开发一个用户友好的软件包，用于在不同参数和干预方案（例如疫苗接种方案）下进行计算机模拟，从而更好地了解如何控制COVID-19的传播。这笔拨款是使用《冠状病毒援助、救济和经济安全（关怀）法案》分配给MPS的补充资金提供的资金。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。

The outbreak of COVID-19 has created a tremendous need for predicting both the dynamics and the size of regional COVID-19 outbreaks. Equally important is the need to determine the potential effects of early interventions such as school closures and mandatory or self-imposed quarantines. To answer these questions, this project will develop a general mathematical framework for analyzing the ongoing outbreak trends using data solely from partially observed new daily infection counts (also known as the epidemic curve). The PI’s new framework will not assume any specific infectious or recovery periods (which are often unknown) or observable prevalence of the disease. The tools developed as part of this project will both help predict the rate of growth of new infections and estimate the effect of social distancing and other preventative measures on flattening the epidemic curve. The PI will use a new dynamical survival analysis approach to predict the trajectory of the COVID-19 epidemic for a mid-western region of the United States. Data from elsewhere in the world, like the city of Wuhan in China, will be used to calibrate the predictions. The project will also provide a practical interdisciplinary training for a PhD student and a post-doctoral fellow.The modeling and predictive framework to be developed is fundamentally different from the traditional approach based on the incidence or prevalence counts in a compartmental SIR model. Specifically, the PI will apply the dynamical survival analysis (DSA) approach that considers aggregated mean field equations for the underlying large stochastic network and regards them as the approximate survival law of the infection times. The PI will use these DSA-based equations to model both the epidemic and recovery curves and compare them with the ones observed during the COVID-19 outbreak. The statistical analysis of epidemic data performed with the help of the new framework will allow the quick elucidation of the dynamics of an epidemic (for example, the basic reproduction number, R0) and the potential impact of interventions (such as quarantine or social distancing). The new framework will help provide a better understanding of how preventive behaviors affect COVID-19 dynamics via changes in the network structure and changes in disease transmission across edges in the network. This project will develop a user-friendly software package for computer simulations under different parameter and intervention scenarios (for example, vaccination schemes) that will lead to a better understanding of how to control COVID-19 transmission.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplemental funds allocated to MPS.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.