Vaccination and SARS-CoV-2 variants: how much containment is still needed? A quantitative assessment
arXiv:2102.08704 · doi:10.1038/s41591-021-01334-5
Abstract
Despite the progress in medical care, combined population-wide interventions (such as physical distancing, testing and contact tracing) are still crucial to manage the SARS-CoV-2 pandemic, aggravated by the emergence of new highly transmissible variants. We combine the compartmental SIDARTHE model, predicting the course of COVID-19 infections, with a new data-based model that projects new cases onto casualties and healthcare system costs. Based on the Italian case study, we outline several scenarios: mass vaccination campaigns with different paces, different transmission rates due to new variants, and different enforced countermeasures, including the alternation of opening and closure phases. Our results demonstrate that non-pharmaceutical interventions (NPIs) have a higher impact on the epidemic evolution than vaccination, which advocates for the need to keep containment measures in place throughout the vaccination campaign. We also show that, if intermittent open-close strategies are adopted, deaths and healthcare system costs can be drastically reduced, without any aggravation of socioeconomic losses, as long as one has the foresight to start with a closing phase rather than an opening one.
Cited by in corpus (8)
- Data-Driven Methods for Present and Future Pandemics: Monitoring, Modelling and Managing
- Asymptotic-Preserving Neural Networks for hyperbolic systems with diffusive scaling
- A model of COVID-19 pandemic evolution in African countries
- Recent trends in socio-epidemic modelling: behaviours and their determinants
- Effects of infection fatality ratio and social contact matrices on vaccine prioritization strategies
- A Quantum Approach for Optimal Transient Control in Network-Based Epidemic Models
- Predicting the effects of waning vaccine immunity against COVID-19 through high-resolution agent-based modeling
- Variant-driven multi-wave pattern of COVID-19 via a Machine Learning analysis of spike protein mutations