.png){kind=icon}
返回
A simulation-optimization framework for optimizing response strategies to epidemics
.png){kind=icon}
OA
AI
DOI：10.1016/j.orp.2021.100210.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Decision support systems
Simulation-optimization
Epidemics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.7
论文数:
288
被引数:
951
机构
引用论文
Degradation of carbon tetrachloride in the presence of iron and sulphur containing compounds.png)
Chemosphere
ChemosphereIF0
PREAI
分享收藏Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China武汉138例2019新型冠状病毒感染肺炎住院患者临床特征分析OAAI
OAAI分享收藏
查看更多内容