标题： 存在未检测感染时流行病的多变量行为变化建模 显示英文标题

标题： Multivariable Behavioral Change Modeling of Epidemics in the Presence of Undetected Infections

摘要： 流行病模型是理解并实施控制传染病传播策略的宝贵工具，同时也能为公共卫生政策和资源分配提供信息。 然而，当前的建模方法存在局限性，这些局限性降低了它们的实际效用，例如忽略了人群对疫情的反应变化，或者忽视了人群中未被检测到的感染者。 这些局限性在新冠疫情期间变得尤为明显，突显出开发更准确且更具信息量的模型的必要性。 受这些挑战的推动，我们开发了一种新的贝叶斯流行病建模框架，通过结合行为反应和未被检测到的感染来更好地捕捉疾病传播的复杂性。 具体而言，我们的框架做出了三项贡献：1）利用住院和死亡数据来建模疾病动态；2）处理因大量无症状和未检测感染而产生的数据不确定性；3）允许人口的行为变化受到多个数据源（病例和死亡人数）的动态影响。 我们通过模拟彻底研究了所提出模型的特性，并在蒙特利尔和迈阿密的新冠数据上展示了其效用。 显示英文摘要

摘要： Epidemic models are invaluable tools to understand and implement strategies to control the spread of infectious diseases, as well as to inform public health policies and resource allocation. However, current modeling approaches have limitations that reduce their practical utility, such as the exclusion of human behavioral change in response to the epidemic or ignoring the presence of undetected infectious individuals in the population. These limitations became particularly evident during the COVID-19 pandemic, underscoring the need for more accurate and informative models. Motivated by these challenges, we develop a novel Bayesian epidemic modeling framework to better capture the complexities of disease spread by incorporating behavioral responses and undetected infections. In particular, our framework makes three contributions: 1) leveraging additional data on hospitalizations and deaths in modeling the disease dynamics, 2) accounting data uncertainty arising from the large presence of asymptomatic and undetected infections, and 3) allowing the population behavioral change to be dynamically influenced by multiple data sources (cases and deaths). We thoroughly investigate the properties of the proposed model via simulation, and illustrate its utility on COVID-19 data from Montreal and Miami.