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BackgroundSchool closures have been a prominent component of the global Coronavirus Disease 2019 (COVID-19) response. However, their effect on viral transmission, COVID-19 mortality and health care system pressure remains incompletely understood, as traditional observational studies fall short in assessing such population-level impacts.Methods and findingsWe used a mathematical model to simulate the COVID-19 epidemics of 74 countries, incorporating observed data from 2020 to 2022 and historical school closure timelines. We then simulated a counterfactual scenario, assuming that schools remained open throughout the study period. We compared the simulated epidemics in terms of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections, deaths, and hospital occupancy pressure. We estimated that school closures achieved moderate to significant burden reductions in most settings over the period 2020 to 2022. They reduced peak hospital occupancy pressure in nearly all countries, with 72 out of 74 countries (97%) showing a positive median estimated effect, and median estimated effect ranging from reducing peak hospital occupancy pressure by 89% in Brazil to increasing it by 19% in Indonesia. The median estimated effect of school closures on COVID-19 deaths ranged from a 73% reduction in Thailand to a 7% increase in the United Kingdom. We estimated that school closures may have increased overall COVID-19 mortality (based on median estimates) in 9 countries (12%), including several European nations and Indonesia. This is attributed to changes in population-level immunity dynamics, leading to a concentration of the epidemic during the Delta variant period, alongside an upward shift in the age distribution of infections. While our estimates were associated with significant uncertainty, our sensitivity analyses exploring the impact of social mixing assumptions revealed robustness in our country-specific conclusions. The main study limitations include the fact that analyses were conducted at the national level, whereas school closure policies often varied by region. Furthermore, some regions, including Africa, were underrepresented due to insufficient data informing the model.ConclusionsOur analysis revealed nuanced effects of school closures on COVID-19 dynamics, with reductions in COVID-19 impacts in most countries but negative epidemiological effects in a few others. We identified critical mechanisms for consideration in future policy decisions, highlighting the unpredictable nature of emerging variants and potential shifts in infection demographics associated with school closures.

Optimal layout problems consist in positioning a given number of components in order to minimize an objective function while satisfying geometrical or functional constraints. Such kinds of problems appear in the design process of aerospace systems such as satellite or spacecraft design. These problems are NP-hard, highly constrained and dimensional. This paper describes a two-stage algorithm combining a genetic algorithm and a quasi-physical approach based on a virtual-force system in order to solve multi-container optimal layout problems such as satellite modules. In the proposed approach, a genetic algorithm assigns the components to the containers while a quasi-physical algorithm based on a virtual-force system is developed for positioning the components in the assigned containers. The proposed algorithm is experimented and validated on the satellite module layout problem benchmark. Its global performance is compared with previous algorithms from the literature.

Bayesian Optimization (BO) with Gaussian process regression is a popular framework for the optimization of time-consuming cost functions. However, the joint exploitation of BO and parallel processing capabilities remains challenging, despite intense research efforts over the last decade. In particular, the choice of a suitable batch-acquisition process, responsible for selecting promising candidate solutions for batch-parallel evaluation, is crucial. Even though some general recommendations can be found in the literature, many of its hyperparameters remain problem-specific. Moreover, the limitations of existing approaches in terms of scalability, especially for moderately expensive objective functions, are barely discussed. This work investigates five parallel BO algorithms based on different batch-acquisition processes, applied to the optimal scheduling of Underground Pumped Hydro-Energy Storage stations and classical benchmark functions. Efficient management of such energy-storage units requires parallel BO algorithms able to find solutions in a very restricted time to comply with the responsive energy markets. Our experimental results show that for the considered methods, a batch of four candidates is a good trade-off between execution speed and relevance of the candidates. Analysis of each method’s strengths and weaknesses indicates possible future research directions.