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

RAPID：使用动态生存分析对 COVID-19 的爆发进行建模

基本信息

批准号：
2027001
负责人：
Grzegorz Rempala
金额：
$ 19.86万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2023-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027001&HistoricalAwards=false
关键词：
RAPID Modeling Outbreak COVID 19

项目摘要

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.

COVID-19的爆发产生了巨大的需求，以预测区域Covid-19的动态和大小。同样重要的是，需要确定早期干预措施的潜在影响，例如学校关闭以及强制性或自我强加的隔离。为了回答这些问题，该项目将开发一个一般的数学框架，用于使用仅部分观察到的新的日常感染计数（也称为流行病曲线）的数据来分析正在进行的爆发趋势。 PI的新框架不会假设该疾病的任何特定的感染或恢复期（通常是未知的）或可观察到的患病率。作为该项目的一部分开发的工具既将有助于预测新感染的增长速度，并估算社会疏远和其他预防措施对扁平流行曲线的影响。 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的轨迹。像中国武汉一样，来自世界其他地方的数据将用于校准预测。该项目还将为博士生和博士后研究员提供实用的跨学科培训。要开发的建模和预测框架与基于事件的传统方法或普遍性计数在隔间sir模型中的数量根本不同。具体而言，PI将采用动态生存分析（DSA）方法，该方法考虑了基础大型随机网络的汇总平均场等值，并将其视为感染时间的近似生存法。 PI将使用这些基于DSA的方程式对流行曲线进行建模，并将其与在199疫情中观察到的方程式进行比较。借助新框架进行的流行数据的统计分析将允许快速阐明流行病的动力学（例如，基本的繁殖数，R0）以及干预措施的潜在影响（例如隔离或社会疏远）。新框架将有助于更好地理解预防行为如何通过网络结构的变化以及跨网络边缘疾病传播的变化来影响Covid-19动态。 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使用基金会的智力优点和更广泛的影响审查标准通过评估来支持。