Background: If SARS-CoV-2 elimination is not feasible, strategies are needed to minimise the impact of COVID-19 in the medium-to-long term, until safe and effective vaccines can be used at the population-level. Methods: Using a mathematical model, we identified contact mitigation strategies that minimised COVID-19-related deaths or years of life lost (YLLs) over a time-horizon of 15 months, using an intervention lasting six or 12 months, in Belgium, France, Italy, Spain, Sweden and the UK. We used strategies that either altered age- or location-specific contact patterns. The optimisation was performed under the constraint that herd immunity should be achieved by the end of the intervention period if post-infection immunity was persistent. We then tested the effect of waning immunity on the strategies. Findings: Strategies of contact mitigation by age were much more effective than those based on mitigation by location. Extremely stringent contact reductions for individuals aged over 50 were required in most countries to minimise deaths or YLLs. The median final proportion of the population ever-infected with SARS-CoV-2 after herd immunity was reached ranged between 30% and 43%, depending on the country and intervention duration. Compared to an unmitigated scenario, optimised age-specific mitigation was predicted to avert over 1 million deaths across the six countries. The optimised scenarios assuming persistent immunity resulted in comparable hospital occupancies to that experienced during the March-April European wave. However, if immunity was short-lived, high burdens were expected without permanent contact mitigation. Interpretation: Our analysis suggests that age-selective mitigation strategies can reduce the mortality impacts of COVID-19 dramatically even when significant transmission occurs. The stringency of the required restrictions in some groups raises concerns about the practicality of these strategies. If post-infection immunity was short-lived, solutions based on a mitigation period designed to increase population immunity should be accompanied with ongoing contact mitigation to prevent large epidemic resurgence.
Surrogate-based optimization is widely used to deal with long-running black-box simulation-based objective functions. Actually, the use of a surrogate model such as Kriging or Artificial Neural Network allows to reduce the number of calls to the CPU time-intensive simulator. Bayesian optimization uses the ability of surrogates to provide useful information to help guiding effectively the optimization process. In this paper, the Efficient Global Optimization (EGO) reference framework is challenged by a Bayesian Neural Network-assisted Genetic Algorithm, namely BNN-GA. The Bayesian Neural Network (BNN) surrogate is chosen for its ability to provide an uncertainty measure of the prediction that allows to compute the Expected Improvement of a candidate solution in order to improve the exploration of the objective space. BNN is also more reliable than Kriging models for high-dimensional problems and faster to set up thanks to its incremental training. In addition, we propose a batch-based approach for the parallelization of BNN-GA that is challenged by a parallel version of EGO, called q-EGO. Parallel computing is a highly important complementary way (to surrogates) to deal with the computational burden of simulation-based optimization. The comparison of the two parallel approaches is experimentally performed through several benchmark functions and two real-world problems within the scope of Tuberculosis Transmission Control (TBTC). The study presented in this paper proves that parallel batched BNN-GA is a viable alternative to q-EGO approaches being more suitable for high-dimensional problems, parallelization impact, bigger databases and moderate search budgets. Moreover, a significant improvement of the solutions is obtained for the two TBTC problems tackled.
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