The inverse dynamics simulation of the musculoskeletal system is a common method to understand and analyse human motion. The ground reaction forces can be accurately estimated by experimental measurements using force platforms. However, the number of steps is limited by the number of force platforms available in the laboratory. Several numerical methods have been proposed to estimate the ground reaction forces without force platforms, i.e., solely based on kinematic data combined with a model of the foot-ground contact. The purpose of this work is to provide a more efficient method, using a unilaterally constrained model of the foot at the center of pressure to compute the ground reaction forces. The proposed model does not require any data related with the compliance of the footground contact and is kept as simple as possible. The indeterminacy in the force estimation is handled using a least square approach with filtering. The relative root mean square error (rRMSE) between the numerical estimations and experimental measurements are 4.1% for the vertical component of the ground reaction forces (GRF), 11.2% for the anterior component and 5.3% for the ground reaction moment (GRM) in the sagittal plane.

Background The role played by large-scale repetitive SARS-CoV-2 screening programs within university populations interacting continuously with an urban environment, is unknown. Our objective was to develop a model capable of predicting the dispersion of viral contamination among university populations dividing their time between social and academic environments. Methods Data was collected through real, large-scale testing developed at the University of Liège, Belgium, during the period Sept. 28th-Oct. 29th 2020. The screening, offered to students and staff (n = 30,000), began 2 weeks after the re-opening of the campus but had to be halted after 5 weeks due to an imposed general lockdown. The data was then used to feed a two-population model (University + surrounding environment) implementing a generalized susceptible-exposed-infected-removed compartmental modeling framework. Results The considered two-population model was sufficiently versatile to capture the known dynamics of the pandemic. The reproduction number was estimated to be significantly larger on campus than in the urban population, with a net difference of 0.5 in the most severe conditions. The low adhesion rate for screening (22.6% on average) and the large reproduction number meant the pandemic could not be contained. However, the weekly screening could have prevented 1393 cases (i.e. 4.6% of the university population; 95% CI: 4.4–4.8%) compared to a modeled situation without testing. Conclusion In a real life setting in a University campus, periodic screening could contribute to limiting the SARS-CoV-2 pandemic cycle but is highly dependent on its environment.

In mid-2020, the University of Liège (ULiège, Belgium) commissioned the ULiège Video Game Research Laboratory (Liège Game Lab) and the AR/VR Lab of the HEC-Management School of ULiège to create a serious game to raise awareness of preventive measures for its university community. This project has its origins in two objectives of the institutional policy of ULiège in response to the crisis caused by SARS-CoV-2 to raise awareness among community members of various preventive actions that can reduce the spread of the virus and to inform about the emergence and progression of a pandemic. After almost two years of design, the project resulted in the creation of SARS Wars, a decision-making management game for browsers and smartphones. This article presents the creative process of the game, specifically the integration of an adapted SEIR (susceptible-exposed-infectious-recovered) model, as well as the modeling of intercompartmental circulation dynamics in the game’s algorithm, and the various limitations observed regarding the game’s original missions and possibilities for future work. The SARS-CoV-2 video game project may be considered an innovative way to translate epidemiology into a language that can be used in the scope of citizen sciences. On the one hand, it provides an engaging tool and encourages active participation of the audience. On the other hand, it allows us to have a better understanding of the dynamic changes of a pandemic or an epidemic (crisis preparedness, monitoring, and control) and to anticipate potential consequences in the given parameters at set time (emerging risk identification), while offering insights for impact on some parameters on motivation (social science aspect).

No abstract

No abstract