Recurrent COVID-19 outbreaks have placed immense strain on the hospital system in Quebec. We develop a Bayesian three-state coupled Markov switching model to analyze COVID-19 outbreaks across Quebec based on admissions in the 30 largest hospitals. Within each catchment area, we assume the existence of three states for the disease: absence, a new state meant to account for many zeroes in some of the smaller areas, endemic and outbreak. Then we assume the disease switches between the three states in each area through a series of coupled nonhomogeneous hidden Markov chains. Unlike previous approaches, the transition probabilities may depend on covariates and the occurrence of outbreaks in neighboring areas, to account for geographical outbreak spread. Additionally, to prevent rapid switching between endemic and outbreak periods we introduce clone states into the model which enforce minimum endemic and outbreak durations. We make some interesting findings, such as that mobility in retail and recreation venues had a positive association with the development and persistence of new COVID-19 outbreaks in Quebec. Based on model comparison our contributions show promise in improving state estimation retrospectively and in real-time, especially when there are smaller areas and highly spatially synchronized outbreaks. Furthermore, our approach offers new and interesting epidemiological interpretations, such as being able to estimate the effect of covariates on disease extinction.

翻译：周期性COVID-19疫情对魁北克的医院系统造成了巨大压力。我们开发了一个贝叶斯三状态耦合马尔可夫转换模型，基于魁北克30家最大医院的入院数据来分析该地区的COVID-19疫情。在每个医疗服务区域内，我们假设疾病存在三种状态：不存在状态（旨在解释部分较小区域中大量零病例的新状态）、地方性流行状态和暴发状态。随后，我们假设每个区域内的疾病状态通过一系列耦合的非齐次隐马尔可夫链在这三种状态间转换。与以往方法不同，本模型的转移概率可依赖于协变量及相邻区域疫情暴发情况，以刻画疫情的地理扩散模式。此外，为避免地方性流行期与暴发期之间的快速切换，我们在模型中引入克隆状态，强制设置最小流行期与暴发期持续时间。研究发现，零售与娱乐场所的人员流动性对魁北克新COVID-19疫情的发生与持续呈现正相关关系。基于模型比较，我们的方法在回顾性及实时状态估计中展现出改进潜力，尤其在涉及较小区域及高度空间同步暴发场景时优势显著。同时，该模型提供了新颖且富有流行病学意义的解读，例如能够评估协变量对疾病灭绝概率的影响。