基于住院人数的魁北克COVID-19爆发的三态耦合马尔可夫切换模型
A three-state coupled Markov switching model for COVID-19 outbreaks across Quebec based on hospital admissions
AI总结：
本文开发了一个贝叶斯三态耦合马尔可夫切换模型，基于魁北克30家最大医院的住院数据，结合协变量与空间传播因素分析COVID-19爆发，改进了状态估计并提供了新的流行病学解释。
摘要：
反复爆发的COVID-19给魁北克的医院系统带来了巨大压力。我们开发了一个贝叶斯三态耦合马尔可夫切换模型，基于30家最大医院的住院人数来分析魁北克各地的COVID-19爆发情况。在每个服务区内，我们假设该疾病存在三种状态：absence（缺席，一种旨在解释部分较小区域众多零值的新状态）、endemic（地方性流行）和outbreak（爆发）。然后我们假设疾病在每个区域内通过一系列耦合的非齐次隐马尔可夫链在这三种状态之间进行切换。与以往方法不同，转移概率可能依赖于协变量和邻近区域爆发的发生，以解释爆发的地理传播。此外，为了防止在地方性流行期和爆发期之间快速切换，我们在模型中引入了克隆状态，以强制执行最短的地方性流行和爆发持续时间。我们得出了一些有趣的发现，例如零售和娱乐场所的流动性与魁北克新COVID-19爆发的发展和持续存在呈正相关。基于模型比较，我们的贡献在回顾性和实时状态估计方面显示出改进的潜力，特别是在存在较小区域和高度空间同步的爆发时。此外，我们的方法提供了新颖且有趣的流行病学解释，例如能够估计协变量对疾病消灭的影响。
英文摘要：
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.
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