RAPID: Analysis of Multiscale Network Models for the Spread of COVID-19

RAPID：针对 COVID-19 传播的多尺度网络模型分析

• 批准号: 2027438
• 负责人: Andrea Bertozzi
• 金额: $ 20万
• 依托单位: University of California-Los Angeles
• 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: 2020-04-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027438&HistoricalAwards=false
• 关键词: RAPID; Analysis; Multiscale; Network; Models

The current pandemic of coronavirus disease 2019 (COVID-19) has upended the daily lives of more than a billion people worldwide, and governments are struggling with the task of responding to the spread of the disease. Uncertainty in transmission rates and the outcomes of social distancing, "shelter-at-home" executive orders, and other interventions have created unprecedented challenges to the United States health care system. This project will address these issues directly using advanced mathematical modeling from dynamical systems, stochastic processes, and networks. The mathematical models, which are formulated with the specific features of COVID-19 in mind, will provide insights that are critical to people on the front lines who need to make recommendations for intervention strategies and human-behavior patterns to best mitigate the spread of this disease in a timely manner. The project will train a postdoctoral scholar, a PhD student, and two undergraduate students in the research needed to solve these complex problems. The standard approach for epidemic modeling, at the community scale and larger, is compartmental models in which individuals are in one of a small number of states (for example, susceptible, infected, recovered, exposed, latent), with individuals moving between states. The COVID-19 epidemic can be modeled in this way, with resistance as part of the dynamics. The simplest examples of such models for large populations are coupled ordinary differential equations that describe the fraction of a population in each of the states. To model the stochasticity of infection and latency, models with self-exciting point processes can be fit to real-world data. This project compares the dynamical systems and stochastic models of relevance to COVID-19 transmission. The models also incorporate network structure for the transmission pathways. The project extends prior research on contagions on multilayer networks by incorporating multiple transmission methods and coupling between the spread of the contagion itself and human behavior patterns. The project leverages high-resolution societal mixing patterns in epidemics, as they influence both (1) observations and demographics of who has been diagnosed with COVID-19 and (2) who transits the disease, sometimes without being diagnosed.This award is co-funded with the Applied Mathematics program and the Computational Mathematics program (Division of Mathematical Sciences), and the Office of Multidisciplinary Activities (OMA) program.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.

目前的2019年冠状病毒病大流行(新冠肺炎)颠覆了全球10多亿人的日常生活，各国政府正在努力应对该疾病的传播。传播率的不确定性和社会距离、“居家避难所”行政命令和其他干预措施给美国卫生保健系统带来了前所未有的挑战。该项目将使用来自动态系统、随机过程和网络的高级数学建模来直接解决这些问题。这些数学模型是考虑到新冠肺炎的具体特征而制定的，将提供对于一线人员至关重要的见解，他们需要为干预策略和人类行为模式提出建议，以最好地及时遏制这种疾病的传播。该项目将培训一名博士后学者、一名博士生和两名本科生进行解决这些复杂问题所需的研究。在社区规模和更大的范围内，流行病建模的标准方法是隔室模型，其中个体处于少数状态之一(例如，易感、感染、恢复、暴露、潜伏)，个体在状态之间移动。新冠肺炎疫情可以这样建模，耐药性是动力学的一部分。这类大人口模型最简单的例子是耦合的常微分方程式，它描述了人口在每个州的比例。为了对感染和潜伏期的随机性进行建模，具有自激点过程的模型可以与真实世界的数据相吻合。本项目比较了与新冠肺炎传播相关的动力系统和随机模型。这些模型还包含了传输路径的网络结构。该项目通过结合多种传播方法以及传染病传播本身和人类行为模式之间的耦合，扩展了先前对多层网络上的传染病的研究。该项目在流行病中利用了高分辨率的社会混合模式，因为它们影响到(1)被诊断出患有新冠肺炎的人的观察和人口统计数据，以及(2)谁在没有被诊断出的情况下传播疾病。该奖项由应用数学计划和计算数学计划(数学科学部)以及多学科活动办公室计划共同资助。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。

项目成果

A martingale formulation for stochastic compartmental susceptible-infected-recovered (SIR) models to analyze finite size effects in COVID-19 case studies

用于随机区室易感感染恢复 (SIR) 模型的鞅公式，用于分析 COVID-19 案例研究中的有限尺寸效应

• DOI: 10.3934/nhm.2022009
• 发表时间: 2022
• 期刊: Networks & Heterogeneous Media
• 影响因子: 1
• 作者: Li, Xia;Wang, Chuntian;Li, Hao;Bertozzi, Andrea L.
• 通讯作者: Bertozzi, Andrea L.

The challenges of modeling and forecasting the spread of COVID-19

• DOI: 10.1073/pnas.2006520117
• 发表时间: 2020-07-21
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Bertozzi, Andrea L.;Franco, Elisa;Sledge, Daniel
• 通讯作者: Sledge, Daniel

Disease Detectives: Using Mathematics to Forecast the Spread of Infectious Diseases

• DOI: 10.3389/frym.2020.577741
• 发表时间: 2020-06
• 作者: Heather Z. Brooks;Unchitta Kanjanasaratool;Yacoub H. Kureh;M. A. Porter

Alternative SIAR models for infectious diseases and applications in the study of non-compliance

• DOI: 10.1142/s0218202522500464
• 发表时间: 2022-11-04
• 期刊: MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCES
• 影响因子: 3.5
• 作者: Bongarti，Marcelo;Galvan，Luke Diego;Bertozzi，Andrea L.
• 通讯作者: Bertozzi，Andrea L.

A multilayer network model of the coevolution of the spread of a disease and competing opinions

• DOI: 10.1142/s0218202521500536
• 发表时间: 2021-11-01
• 期刊: MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCES
• 影响因子: 3.5
• 作者: Peng, Kaiyan;Lu, Zheng;Porter, Mason A.
• 通讯作者: Porter, Mason A.