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.

项目摘要/摘要 已经开发了越来越多的COVID传输模型，以帮助预测正在进行的表演 Demic和比较不同非药物干预措施（NPI）的结果 和医疗供应需求。这些模型中的大多数不包括自适应行为效应，以描述风险如何 感知和疲劳影响社会疏远和减少传播的影响。决定 戴口罩，社会接触水平和疫苗接种将定义流行病是受到控制还是进入 年流通。我们提出了基于人群（PBM）和基于代理（ABM）传输的发展 - 研究个人行为和传播动态之间的相互作用的SION模型，同时考虑 仍然围绕病毒的许多不确定性，例如季节性影响和免疫力丧失。添加- Ally，我们的模型将用于研究COVID-19和季节性影响以及相对行为如何相互作用， 加剧结果，并有可能压倒医疗保健系统。这些模型将基于我们先前的 研究。自2016年秋季以来 对季节性流体疫苗接种的看法。从这些数据构建的ABM模型 解释了过去经验，同伴效应和人口异质性的适应和记忆。使用机器 学习方法，我们增强了一个合成网络，该网络代表了一个以这种行为为代表美国的小城市 数据。我们继续进行这些调查的修改版本，以跟踪这些信念如何转化为 新冠肺炎。同时，我们开发了一个基于隔室的Covid-19模型，该模型模型 传播以及NPI强度和时间对健康和经济结果的影响。我们建议 扩展我们当前的室内PBM并建立一个新的个体级ABM，并由纵向调查告知。 我们将进行四年的纵向小组调查，以构建一个决策的经验行为模型 至社会距离，参与传输减少措施（例如戴面膜），然后进行疫苗接种。这 信息将与我们现有的合成网络数据集结合使用，以使我们能够建立个人级别 Covid-19在美国代表城市中的ABM与我们的流体ABM融合在一起。这个模型将 捕获单个行为如何影响宏观疾病传播以及如何流动和COVID-19 可以互动。来自我们个人级别模型的洞察力和数据将用于告知和参数化适应性 我们的车间级模型中的行为，允许在美国各个州进行策略比较。在 此外，我们将考虑哪些政策对关键行为和技术不确定性，例如 响应感知风险以及疫苗的时间和有效性的行为变化程度。最后， 我们将开发基于Web的交互式工具，以允许探索和比较 各种潜在期货。