Bayesian inference; COVID-19; Infectious disease transmission; Screening testing; Simulation-based inference; Stochastic dynamics; Disease transmission; Hybrid stochastic model; Infectious disease; University campus; Statistics and Probability; Modeling and Simulation; Immunology and Microbiology (all); Applied Mathematics
[en] 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.
Funding text :
The authors thank the members of the University of Li?ge's Risk Analysis Group, established in the context of the COVID-19 pandemic, for many relevant discussions and for providing the data. The first author would also like to acknowledge Romin Tomasetti for helpful discussions relevant to the machine-learning simulation-based inference approach.