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: 10678677
• 负责人: Andrew Parker
• 金额: $ 56.39万
• 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/10678677
• 关键词: Accounting; Adaptive Behaviors; Affect; Age; Attitude; Back; Behavior; Behavioral; Behavioral Model; Belief; COVID-19; COVID-19 pandemic; Calibration; Cessation of life; Characteristics; Circulation; Cities; Clinical Medicine; Communicable Diseases; Complex; Coupled; Data; Data Set; Decision Making; Development; Disease; Effectiveness; Ensure; Epidemic; Epidemiology; Fatigue; Funding; Funding Agency; Future; Health; Healthcare Systems; Heterogeneity; Immunity; Individual; Infection; Influenza; Intervention; Longitudinal Studies; Longitudinal Surveys; Masks; Measures; Memory; Methods; Modeling; National Institute of Allergy and Infectious Disease; Online Systems; Outcome; Pattern; Perception; Periodicals; Persons; 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; Technology; Testing; Translating; US State; Uncertainty; Vaccinated; Vaccination; Vaccines; Variant; Virulent; Virus; Virus Diseases; Work; behavior change; behavioral response; comparison intervention; decision research; disease transmission; economic outcome; experience; falls; 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 contact; 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.

项目总结/摘要 越来越多的COVID-19传播模型已被开发出来，以帮助预测正在进行的epi。 流行病，并比较不同的非药物干预（NPI）的结果，在病例，死亡， 医疗用品需求。这些模型中的大多数不包括描述风险如何影响的适应性行为效应。 认知和疲劳与社交距离和传播减少的互动。决定 戴口罩、社会接触水平和疫苗接种将决定疫情是否得到控制或进入 年度循环。我们建议发展基于人口（PBM）和基于代理（ABM）的传输， 锡永来研究个人行为和传播动力学之间的相互作用，同时考虑到 该病毒仍存在许多不确定因素，如季节性影响和免疫力丧失。加入─ 此外，我们的模型将用于研究COVID-19和季节性疾病如何在科鲁恩萨和各自的行为相互作用， 恶化结果，并可能使医疗保健系统不堪重负。这些模型将建立在我们之前的 research.自2016年秋季以来，我们定期进行纵向调查，调查对风险的态度， 对马丘比丘季节性流感疫苗接种的看法和倾向。根据这些数据构建的ABM模型 解释过去经验的适应和记忆、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一个代表美国小城市的合成网络。 数据我们继续进行这些调查的修改艾德版本，以跟踪这些信念如何转化为 2019冠状病毒病。与此同时，我们开发了一个基于COVID-19人群的房室模型， 传播和NPI强度和时间对健康和经济结果的影响。我们建议 扩展我们目前的分区PBM，并建立一个新的个人层面的ABM，通过纵向调查。 我们将进行一项为期四年的纵向跟踪调查，以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（如戴口罩），并接种疫苗。这 信息将与我们现有的合成网络数据集相结合，使我们能够建立一个个人水平 COVID-19在美国一个代表性城市传播的ABM，与我们在巴塞罗那的ABM相结合。这种模式的 捕捉个人行为如何影响宏观层面的疾病传播，以及在新冠病毒和COVID-19中如何影响 可以互动。来自我们个人层面模型的见解和数据将用于告知和参数化自适应 行为在我们的隔间级模型，允许跨美国各州的政策比较。在 此外，我们还将考虑哪些政策对关键的行为和技术不确定性具有鲁棒性，例如 对感知风险的行为改变程度以及疫苗接种的时间和有效性。最后， 我们将开发基于网络的互动工具，以便在一个 各种潜在的未来。