World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020. Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives due to the illness by September 3, 2020. Prediction of the COVID-19 pandemic will enable policymakers to optimize the use of healthcare system capacity and resource allocation to minimize the fatality rate. In this research, we design a novel hybrid reinforcement learning-based algorithm capable of solving complex optimization problems. We apply our algorithm to several well-known benchmarks and show that the proposed methodology provides quality solutions for most complex benchmarks. Besides, we show the dominance of the offered method over state-of-the-art methods through several measures. Moreover, to demonstrate the suggested method’s efficiency in optimizing real-world problems, we implement our approach to the most recent data from Quebec, Canada, to predict the COVID-19 outbreak. Our algorithm, combined with the most recent mathematical model for COVID-19 pandemic prediction, accurately reflected the future trend of the pandemic with a mean square error of 6.29E−06. Furthermore, we generate several scenarios for deepening our insight into pandemic growth. We determine essential factors and deliver various managerial insights to help policymakers making decisions regarding future social measures.

中文翻译：

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设计一种基于混合强化学习的算法，用于预测魁北克的 COVID-19 大流行

世界卫生组织 (WHO) 于 2020 年 3 月将 COVID-19 列为大流行病。从那时起，全球报告了 26,795,847 例病例，到 2020 年 9 月 3 日，已有 878,963 人因该病丧生。对 COVID-19 大流行的预测将使政策制定者能够优化医疗保健系统容量和资源分配的使用，以最大限度地降低死亡率。在这项研究中，我们设计了一种新的基于混合强化学习的算法，能够解决复杂的优化问题。我们将我们的算法应用于几个著名的基准测试，并表明所提出的方法为大多数复杂的基准测试提供了高质量的解决方案。此外，我们通过多种措施展示了所提供的方法相对于最先进的方法的优势。此外，为了证明建议的方法在优化实际问题方面的效率，我们对来自加拿大魁北克的最新数据实施了我们的方法，以预测 COVID-19 的爆发。我们的算法结合最新的 COVID-19 大流行预测数学模型，准确反映了大流行的未来趋势，均方误差为 6.29E-06。此外，我们生成了几种情景来加深我们对大流行增长的洞察力。我们确定基本因素并提供各种管理见解，以帮助决策者就未来的社会措施做出决策。我们生成了几种情景来加深我们对流行病增长的洞察力。我们确定基本因素并提供各种管理见解，以帮助决策者就未来的社会措施做出决策。我们生成了几种情景来加深我们对流行病增长的洞察力。我们确定基本因素并提供各种管理见解，以帮助决策者就未来的社会措施做出决策。