摘要：Epidemics require dynamic response strategies that encompass a multitude of policy alternatives and thatbalance health, economic and societal considerations. We propose a simulation–optimization framework toaid policymakers select closure, protection and travel policies to minimize the total number of infectionsunder a limited budget. The proposed framework combines a modified, age-stratified SEIR compartmentalmodel to evaluate the health impact of response strategies and a Genetic Algorithm to effectively search forbetter strategies. We implemented our framework on a real case study in Nova Scotia to devise optimizedresponse strategies to COVID-19 under different budget scenarios and found a clear trade-off between healthand economic considerations. Closure policies seem to be the most sensitive to policy restrictions, followedby travel policies. On the other hand, results suggest that practising social distancing and wearing masks arenecessary whenever their economic impacts are bearable. The framework is generic and can be extended toencompass vaccination policies and to use different epidemiological models and optimization methods.
关键词：Decision support systems;Simulation–optimization;Epidemics
