Decision-based interactive model to determine re-opening conditions of a large university campus in Belgium during the first COVID-19 wave

Abstract

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.

Developing a Video Game as an Awareness and Research Tool Based on SARS-CoV-2 Epidemiological Dynamics and Motivational Perspectives

“…Te University of Liège (ULiège, Belgium) has set up a research and decision think tank in response to the global pandemic development back in early 2020. Tis group was tasked to set up and support various research projects aiming to study the functioning of the SARS-CoV-2, including regular screening of the spread of coronavirus through the development of a distribution system on a voluntary basis and analysis of salivary tests developed by ULiège, applied within the university community [7] and in Belgian nursing homes [8]; developing a compartmental SEIR (susceptible-exposed-infected-recovered) ULiège model based on the testing strategy [9]; and initiating a seroprevalence study of SARS-CoV-2 antibodies based on the analysis of salivary tests among the university population, front-line hospital staf at the University Hospital of Liège, immunocompromised patients, and others sufering from Alzheimer's disease, as well as their caregivers [10]. Tese projects were made possible by the investment of ULiège's GIGA interdisciplinary Biomedical Research Center and the service for data collection and analysis and strategically useful information (RADIUS) in providing extensive analytical data.…”

“…Te elements of the sample travel from one cell to another in a specifc direction. For the SARS-CoV-2 pandemic, ULiège has developed an augmented SEIR-type model [9], which considers not only the salivary testing campaign implemented by ULiège but also the circulation of individuals with the outside world (compartments colored red) (see Figure 2).…”

“…Other points to mention are the involvement of citizens in the various stages of the scientifc process at the origin of the approach (4) while giving them feedback on the project (5). Finally, a last part concerns the production of scientifc data, with the consideration of the project involved as a real research project, with limitations and biases (6); the publication of data and metadata in open access (7); the recognition of citizens as coauthors of this data (8); the evaluation of the project according to scientifc criteria of quality (9); and ethics, confdentiality, and environmental impact (10). According to these criteria, the postcreation review of SARS Wars shows a partial adequacy to the principles of citizen sciences.…”

Modeling optimal reopening strategies for COVID-19 and its variants by keeping infections low and fixing testing capacity

“…Previous models of COVID-19 spread have analyzed the dynamics of disease spread with the adherence and non-adherence of social behavior protocols such as masking, social distancing, and the enforcement of closures/lock downs [14][15][16][17][18][19][20][21][22][23]. To our knowledge, few models have incorporated the effect of randomized daily testing (although many universities have used this strategy to mitigate disease spread [24][25][26]) with the goal of maximizing in-person time utilizing feedback mechanisms while maintaining a low number of infections.…”

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

“… [3] had adopted for both the transmission dynamics and the periodic screening a continuous-time representation, according to which changes in infectiousness status happen and cases are detected continuously at rates that are constant in time. In previous work [9] , we had extended Paltiel et al.’s model by including a larger surrounding community, which we had endowed with its own compartments to study the transmission in the surrounding population and its interaction with the transmission in the university population and the screening. However, important aspects of periodic screening, such as the temporally discrete nature of consecutive tests and the interplay between the duration of the infectious period and the period between consecutive tests, can be difficult to capture with a continuous-time representation of the periodic screening.…”