Modeling the Coupled Dynamics of COVID-19 Transmission and Protective Behaviors

对 COVID-19 传播和保护行为的耦合动态进行建模

• 批准号: 10365006
• 负责人: Andrew Parker
• 金额: $ 60.58万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-17 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10365006
• 关键词: Accounting; Adaptive Behaviors; Affect; Age; Ally; Attitude; Back; Behavior; Behavioral; Behavioral Model; Belief; Blood Circulation; COVID-19; COVID-19 pandemic; Cessation of life; Characteristics; Cities; Clinical Medicine; Communicable Diseases; Complex; Coupled; Data; Data Set; Decision Making; Development; Disease; Effectiveness; Ensure; Epidemic; Epidemiology; Fatigue; Funding; Future; Geographic state; Healthcare Systems; Heterogeneity; Immunity; Individual; Infection; Influenza; Influenza vaccination; Intervention; Longitudinal Studies; Longitudinal Surveys; Masks; Measures; Memory; Methods; Modeling; National Institute of Allergy and Infectious Disease; Online Systems; Outcome; Pattern; Perception; Periodicity; Policies; Population Heterogeneity; Psychology; Reaction; Research; Resources; Respiratory Signs and Symptoms; Respiratory Tract Infections; Respondent; Risk; Risk Behaviors; Running; SARS-CoV-2 transmission; Sampling; Seasons; Shapes; Signal Transduction; Social Distance; Social Network; Speed; Structure; Surveys; Testing; Translating; Uncertainty; Vaccinated; Vaccination; Vaccines; Variant; Virulent; Virus; Virus Diseases; Work; base; behavior change; behavioral response; comparison intervention; decision research; disease transmission; economic outcome; experience; falls; flu transmission; health economics; insight; interactive tool; machine learning method; mathematical model; medical supply; models and simulation; novel vaccines; outcome forecast; pandemic disease; peer; population based; prevent; protective behavior; response; risk perception; seasonal influenza; social; social media; socioeconomics; tool; transmission process; user-friendly; vaccine access; vaccine effectiveness

Project Summary/Abstract A growing number of COVID-19 transmission models have been developed to help forecast the on-going epi- demic and compare outcomes of different non-pharmaceutical interventions (NPIs) in terms of cases, deaths, and medical supply needs. Most of these models do not include adaptive behavioral effects describing how risk perceptions and fatigue influence engagement with social distancing and transmission reduction. Decisions on mask-wearing, levels of social contact, and vaccination will define whether the epidemic is controlled or enters annual circulation. We propose the development of population-based (PBM) and agent-based (ABM) transmis- sion models to study the interplay between individual behavior and transmission dynamics, while considering the many uncertainties which still surround the virus, such as seasonal effects and the loss of immunity. Addition- ally, our models will be used to study how COVID-19 and seasonal influenza and respective behaviors interact, exacerbate outcomes, and potentially overwhelm the health care system. These models will build upon our prior research. Since Fall 2016 we have conducted regular longitudinal surveys investigating attitudes towards, risk perceptions of, and propensity to vaccinate for seasonal influenza. The ABM models constructed from these data account for adaption and memory of past experiences, peer effects, and population heterogeneity. Using machine learning methods, we have augmented a synthetic network representative of a small US city with this behavioral data. We have continued to conduct modified versions of these surveys to track how these beliefs translate to COVID-19. In parallel, we have developed a compartmental population-based model of COVID-19, which models transmission and the effects of NPI intensity and timing on both health and economic outcomes. We propose to extend our current compartmental PBM and build a new individual-level ABM, informed by longitudinal surveys. We will conduct a four-year longitudinal panel survey to construct an empirical behavioral model for decisions to socially distance, engage in transmission reduction measures (such as mask-wearing), and vaccinate. This information will be combined with our existing synthetic network data-set to enable us to build an individual level ABM of the spread of COVID-19 in a representative US city, integrated with our influenza ABM. This model will capture both how individual behaviors impact macro-level disease transmission and how influenza and COVID-19 could interact. Insights and data from our individual-level model will be used to inform and parameterize adaptive behavior within our compartment-level model, allowing for policy comparisons across a range of US states. In addition, we will consider which policies are robust to key behavioral and technological uncertainties, such as the extent of behavior change in response to perceived risk and the timing and effectiveness of vaccines. Finally, we will develop web-based interactive tools that allow for the exploration and comparison of different policies in a variety of potential futures.

项目概要/摘要 越来越多的 COVID-19 传播模型已被开发出来，以帮助预测正在发生的疫情 流行病并比较不同非药物干预措施 (NPI) 在病例、死亡、 大多数这些模型不包括描述风险如何发生的适应性行为效应。 看法和疲劳会影响社交距离和减少传播的决策。 戴口罩、社交接触程度和疫苗接种将决定疫情是否得到控制或进入 我们建议发展基于人口（PBM）和基于代理（ABM）的传播。 sion 模型来研究个体行为和传播动态之间的相互作用，同时考虑 该病毒仍然存在许多不确定性，例如季节性影响和免疫力的丧失。 我们的模型将用于研究 COVID-19 和季节性流感以及各自的行为如何相互作用， 这些模型将建立在我们之前的基础上。 自 2016 年秋季以来，我们定期进行纵向调查，调查人们对风险的态度。 根据这些数据构建的 ABM 模型。 考虑过去经历的适应和记忆、同伴效应和群体异质性使用机器。 学习方法，我们用这种行为增强了代表美国小城市的合成网络 我们继续对这些调查进行修改版本，以跟踪这些信念如何转化为数据。 与此同时，我们开发了一个基于群体的 COVID-19 模型，该模型 我们建议：传播以及 NPI 强度和时间对健康和经济结果的影响。 扩展我们当前的分区 PBM，并根据纵向调查建立新的个人级 ABM。 我们将进行为期四年的纵向面板调查，构建决策的实证行为模型 保持社交距离，采取减少传播的措施（例如戴口罩）并接种疫苗。 信息将与我们现有的合成网络数据集相结合，使我们能够构建个人级别 该模型将与我们的流感 ABM 相结合，对美国代表性城市中的 COVID-19 传播进行 ABM。 捕捉个人行为如何影响宏观层面的疾病传播以及流感和 COVID-19 的影响 来自我们个人级模型的见解和数据将用于通知和参数化自适应。 我们的隔间级别模型中的行为，允许在美国一系列州之间进行政策比较。 此外，我们将考虑哪些政策对关键行为和技术不确定性具有稳健性，例如 最后，针对感知风险的行为改变程度以及疫苗的时机和有效性。 我们将开发基于网络的交互式工具，以便在不同的环境中探索和比较不同的政策 各种潜在的未来。