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

项目摘要/摘要 越来越多的新冠肺炎传导模型已被开发出来，以帮助预测正在进行的环境影响因素。 说明并比较不同非药物干预措施(NPI)在病例、死亡、 和医疗供应需求。这些模型中的大多数都不包括描述风险如何 fl参与社交距离和传播减少的知觉和疲劳。关于以下方面的决定 无论疫情是否得到控制或进入，戴口罩、社会接触的程度和接种疫苗都会影响fi 年度发行量。我们提出了基于种群的(PBM)和基于主体的(ABM)传递的发展。 Sion模型来研究个体行为和传播动力学之间的相互作用，同时考虑到 仍然围绕着病毒的许多不确定因素，如季节性影响和免疫力丧失。添加- 此外，我们的模型将用于研究新冠肺炎和季节性在fluenza如何相互作用，以及各自的行为。 这会加剧后果，并可能使医疗保健系统不堪重负。这些型号将建立在我们以前的基础上 研究。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fl乌恩扎季节性接种疫苗的看法和倾向。根据这些数据构建的ABM模型 解释了对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，我们用这个行为扩充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莫迪fi版，以跟踪这些信念如何转化为 新冠肺炎。同时，我们开发了基于划分人口的新冠肺炎模型，该模型 传播以及NPI强度和时机对健康和经济结果的影响。我们建议 通过纵向调查，扩展我们目前的分组PBM，建立一个新的个人层面的ABM。 我们将进行为期四年的纵向小组调查，以构建决策的经验行为模型 为了保持社交距离，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，使我们能够建立个人级别 新冠肺炎在美国一个有代表性的城市的传播，与我们在fl的ABM整合。这款车型将 捕捉个人行为如何影响宏观水平的疾病传播以及在fluenza和新冠肺炎中如何 可以相互作用。我们将使用来自个人级别模型的见解和数据来通知和参数化自适应 我们的间隔级模型中的行为，允许对美国一系列州的政策进行比较。在……里面 此外，我们将考虑哪些政策对关键的行为和技术不确定性是稳健的，例如 行为改变的程度，以应对感知的风险以及疫苗的时间和有效性。最后， 我们将开发基于Web的交互工具，以便在 各种潜在的期货。

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.