Impact of the representation of contact data on the evaluation of interventions in infectious diseases simulations (preprint)

Computational models offer a unique setting to test strategies to mitigate infectious diseases’ spread, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency: many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high resolution contact data, we use data representations ranging from very detailed to very coarse to inform a model for the spread of SARS-CoV-2 and simulate several mitigation strategies. We find that coarse data representations underestimate the risk of super-spreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs that may trigger the adoption of protocols, as these may depend on data representation.

Minimizing school disruption under high incidence conditions due to the Omicron variant in early 2022 (preprint)

As record cases due to the Omicron variant are currently registered in Europe, schools remain a vulnerable setting suffering large disruption. Extending previous modeling of SARS-CoV-2 transmission in schools in France, we estimate that at high incidence rates reactive screening protocols (as currently applied in France) require comparable test resources as weekly screening (as currently applied in some Swiss cantons), for considerably lower control. Our findings can be used to define incidence levels triggering school protocols and optimizing their cost-effectiveness.

Self-testing and vaccination against COVID-19 to minimize school closure (preprint)

Schools were largely closed in 2020-2021 to counter COVID-19 spread, impacting students education and well-being. With highly contagious variants expanding in Europe while vaccine hesitancy persists, safe options to maintain schools open are urgently needed. We developed an agent-based model of SARS-CoV-2 transmission in school. We used empirical contact data measured in a primary and a secondary school in France, and field estimates for adherence to screening from 683 schools during the spring 2021 wave. Examining different screening protocols, we performed a cost-benefit analysis for varying epidemic conditions and vaccination scenarios. In a partially immunized school population, weekly screening would reduce the number of cases on average by 24% in the primary and 53% in the secondary school compared to symptom-based testing alone, if R=1.3 and 50% adhered to screening. This adherence was met in primary schools (53% (95% confidence interval 21-85%)), but insufficient participation was recorded in secondary schools (10% (1-38%) in middle schools, 6% (2-12%) in high schools). Regular screening would also reduce by 90% the number of student-days lost compared to reactive class closure. No difference was predicted when fully vaccinating teachers, due to their limited number and mixing. Partially vaccinating adolescents would still require regular screening for additional control (20% case reduction with 50% vaccinated students). In the upcoming fall, COVID-19 epidemic will likely continue to pose a risk to the safe opening of schools. Increasing vaccination coverage in adolescents and implementing regular testing while largely incentivizing adherence are essential steps to keep schools open.