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.

2019年冠状病毒病（COVID-19）目前的大流行使全球超过十亿人口的日常生活更加颠覆，政府正在努力应对疾病传播的任务。传播率的不确定性和社会疏远，“居住在家”行政命令以及其他干预措施的不确定性对美国卫生保健系统造成了前所未有的挑战。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直接解决这些问题。考虑到Covid-19的特定特征的数学模型将为您提供对前线的人们至关重要的见解，这些人需要为干预策略和人类行为模式提出建议，以最大程度地减轻这种疾病的传播。该项目将在解决这些复杂问题所需的研究中培训一名博士后学者，一名博士生和两名本科生。在社区规模和较大范围内，流行建模的标准方法是隔间模型，其中个人位于少数州之一（例如，易感性，感染，恢复，暴露，潜在），个人在国家之间移动。 COVID-19的流行病可以以这种方式建模，并作为动力学的一部分进行抗性。此类模型的最简单示例是耦合的普通微分方程，这些方程描述了每个州人口的比例。为了建模感染和潜伏期的随机性，具有自我激发点过程的模型可以适合实际数据。该项目比较了与COVID-19传输相关性的动力学系统和随机模型。这些模型还结合了传输途径的网络结构。该项目通过合并多种传输方法并在传播本身的传播和人类行为模式之间进行耦合，从而扩展了对多层网络传染的先前研究。 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)该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。

项目成果

An Analysis of COVID-19 Knowledge Graph Construction and Applications

• DOI: 10.1109/bigdata52589.2021.9671479
• 发表时间: 2021-01-01
• 期刊: 2021 IEEE International Conference on Big Data (Big Data)
• 作者: Flocco, Dominic;Palmer-Toy, Bryce;Jeffrey Brantingham, P.
• 通讯作者: Jeffrey Brantingham, P.

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.

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

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

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.