A simulation–optimization framework for optimizing response strategies to epidemics. (2021)

• Record Type: Journal Article
• Authors: Gillis, Melissa; Urban, Ryley; Saif, Ahmed; Kamal, Noreen; Murphy, Matthew
• Is Part Of: Operations research perspectives. Volume 8(2021)
• Publication Date: 2021
• Subjects: Decision support systems -- Simulation–optimization -- Epidemics; Operations research -- Periodicals; Management science -- Periodicals; 658.403405
• DOI: 10.1016/j.orp.2021.100210
• Languages: English
• ISSNs: 2214-7160

Abstract

Abstract: Epidemics require dynamic response strategies that encompass a multitude of policy alternatives and that balance health, economic and societal considerations. We propose a simulation–optimization framework to aid policymakers select closure, protection and travel policies to minimize the total number of infections under a limited budget. The proposed framework combines a modified, age-stratified SEIR compartmental model to evaluate the health impact of response strategies and a Genetic Algorithm to effectively search for better strategies. We implemented our framework on a real case study in Nova Scotia to devise optimized response strategies to COVID-19 under different budget scenarios and found a clear trade-off between health and economic considerations. Closure policies seem to be the most sensitive to policy restrictions, followed by travel policies. On the other hand, results suggest that practising social distancing and wearing masks are necessary whenever their economic impacts are bearable. The framework is generic and can be extended to encompass vaccination policies and to use different epidemiological models and optimization methods. Highlights: A decision support tool for optimizing response strategies to epidemics is developed. Three types of policies are considered: closure, protection, and travel. A modified SEIR epidemiological model is used to evaluate response strategies. Simulation–optimization is implemented using a Genetic Algorithm. The objective is to achieve a trade-off between health and economic considerations.