A hybrid stochastic model and its Bayesian identification for infectious disease screening in a university campus with application to massive COVID-19 screening at the University of Liège.

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.

Gender effect on the scapular 3D posture and kinematic in healthy subjects.

Populations considered for shoulder analysis are often composed of various ratios of men and women. It is consequently hypothesized that gender has no significant effect on the joint kinematic. However, the literature reports, for the shoulder, differences in the range of motion between genders. The specific influence of gender on the scapulo-thoracic kinematics has not been studied yet. The dominant shoulder of two populations of men and women composed of 11 subjects each were evaluated in three dimensions for three distinct motions: flexion in the sagittal plane, abduction in the frontal plane and gleno-humeral internal/external rotation with the arm abducted at 90°. Posture, kinematics and range of motion were studied separately. For flexion and abduction and with regard to the scapular kinematic, external rotation was significantly larger for women than men. The differences were of at least 5° at 120° of humeral elevation. Upward rotations were identical. Women also showed larger average active humero-thoracic range of motion. The mean differences were of 13°, 7°, 12° and 5° for abduction, flexion, internal rotation and external rotation, respectively. No difference was observed between the scapular resting positions of both populations. The observed differences concerning both the scapular and humeral patterns would indicate that the shoulder behaviour of men and women should not be expected to be similar.