Faculty of Applied Sciences, Université de Liège, 4000 Liège, Belgium.
Immunology-Vaccinology, FARAH, Université de Liège, 4000 Liège, Belgium.
Math Biosci. 2022 May;347:108805. doi: 10.1016/j.mbs.2022.108805. Epub 2022 Mar 16.
Amid the COVID-19 pandemic, universities are implementing various prevention and mitigation measures. Identifying and isolating infectious individuals by using screening testing is one such a measure that can contribute to reducing spread. Here, we propose a hybrid stochastic model for infectious disease transmission in a university campus with screening testing and its surrounding community. Based on a compartmental modeling strategy, this hybrid stochastic model represents the evolution of the infectious disease and its transmission using continuous-time stochastic dynamics, and it represents the screening testing as discrete stochastic events. We also develop, in a Bayesian framework, the identification of parameters of this hybrid stochastic model, including transmission rates. These parameters were identified from the screening test data for the university population and observed incidence counts for the surrounding community. We implement the exploration of the Bayesian posterior using a machine-learning simulation-based inference approach. The proposed methodology was applied in a retrospective modeling study of a massive COVID-19 screening conducted at the University of Liège in Fall 2020. The emphasis of the paper is on the development of the hybrid stochastic model to assess the impact of screening testing as a measure to reduce spread. The hybrid stochastic model allows various factors to be represented and examined, such as interplay with the surrounding community, variability of the transmission dynamics, the rate of participation in the screening testing, the test sensitivity, the test frequency, the diagnosis delay, and compliance with isolation. The application in the retrospective modeling study suggests that a high rate of participation and a high test frequency are important factors to reduce spread.
在 COVID-19 大流行期间，各大学正在实施各种预防和缓解措施。通过使用筛查检测来识别和隔离感染个体是一项有助于减少传播的措施。在这里，我们提出了一种具有筛查检测及其周围社区的大学园区传染病传播的混合随机模型。基于分区建模策略，该混合随机模型使用连续时间随机动力学来表示传染病及其传播的演变，并将筛查检测表示为离散随机事件。我们还在贝叶斯框架中开发了这种混合随机模型参数的识别，包括传播率。这些参数是从大学人群的筛查测试数据和周围社区的观察发病率中识别出来的。我们使用基于机器学习模拟的推理方法来探索贝叶斯后验。该方法应用于 2020 年秋季在列日大学进行的大规模 COVID-19 筛查的回顾性建模研究。本文的重点是开发混合随机模型来评估筛查检测作为减少传播的措施的影响。混合随机模型允许代表和检查各种因素，例如与周围社区的相互作用、传播动态的可变性、筛查检测的参与率、检测灵敏度、检测频率、诊断延迟和隔离的遵守情况。在回顾性建模研究中的应用表明，高参与率和高检测频率是减少传播的重要因素。
