14737Texas A&M University Commerce, Commerce, TX, USA.
Health Informatics J. 2022 Jul-Sep;28(3):14604582221120344. doi: 10.1177/14604582221120344.
Covid-19 has impacted the lives of people across the world with deaths and unprecedented economic damage. Countries have employed various restrictions and lockdowns to slow down the rate of its spread with varying degrees of success. This research aims to propose an optimal strategy for dealing with a pandemic taking the deaths and economy into account. A complete lockdown until vaccination is not suitable as it can destroy the economy, whereas having no restrictions would result in more Covid-19 cases. Therefore, there is a need for a dynamic model which can propose a suitable strategy depending on the economic and health situation. This paper discusses an approach involving a systems dynamics model for evaluating deaths and hospitals and a fuzzy inference system for deciding the strategy for the next time period based on pre-defined rules. We estimated Gross Domestic Product (GDP) as a sum of government spending, investment, consumption, and spending. The resulting hybrid framework aims to attain a balance between health and economy during a pandemic. The results from a 30-week simulation indicate that the model has 2.9 million $ in GDP higher than complete lockdown and 21 fewer deaths compared to a scenario with no restrictions. The model can be used for the decision-making of restriction policies by configuring the fuzzy rules and membership functions. The paper also discusses the possibility of introducing virus variants in the model.
Covid-19 对全球人民的生活造成了影响，导致了死亡和前所未有的经济损失。各国采取了各种限制和封锁措施来减缓其传播速度，但效果各不相同。本研究旨在提出一种考虑死亡和经济因素的应对大流行病的最佳策略。完全封锁直到接种疫苗是不合适的，因为这会破坏经济，而没有限制则会导致更多的新冠病例。因此，需要一种能够根据经济和健康状况提出合适策略的动态模型。本文讨论了一种涉及评估死亡和医院的系统动力学模型以及基于预定义规则为下一个时间段决定策略的模糊推理系统的方法。我们将国内生产总值（GDP）估计为政府支出、投资、消费和支出的总和。由此产生的混合框架旨在在大流行期间实现健康和经济之间的平衡。30 周模拟的结果表明，与完全封锁相比，该模型的 GDP 高出 290 万美元，与没有限制的情况相比，死亡人数减少了 21 人。通过配置模糊规则和隶属函数，该模型可用于限制政策的决策。本文还讨论了在模型中引入病毒变体的可能性。
