Behnam Arman, Jahanmahin Roohollah
Industrial Engineering Department, Iran University of Science and Technology (IUST), Narmak, 16846-13114 Tehran, Iran.
Model Earth Syst Environ. 2022;8(1):579-589. doi: 10.1007/s40808-021-01086-8. Epub 2021 Jan 30.
World is now experiencing the new pandemic caused by COVID-19 virus and all countries are affected by this disease specially Iran. From the beginning of the outbreak until April 30, 2020, over 90,000 confirmed cases of COVID-19 have been reported in Iran. Due to socio-economic problems of this disease, it is required to predict the trend of the outbreak and propose a beneficial method to find out the correct trend. In this paper, we compiled a dataset including the number of confirmed cases, the daily number of death cases and the number of recovered cases. Furthermore, by combining case number variables like behavior and policies that are changing over time and machine-learning (ML) algorithms such as logistic function using inflection point, we created new rates such as weekly death rate, life rate and new approaches to mortality rate and recovery rate. Gaussian functions show superior performance which is helpful for government to improve its awareness about important factors that have significant impacts on future trends of this virus.
世界正在经历由新冠病毒引起的新的大流行，所有国家都受到这种疾病的影响，尤其是伊朗。从疫情爆发开始到2020年4月30日，伊朗已报告超过9万例新冠确诊病例。由于这种疾病带来的社会经济问题，需要预测疫情的发展趋势，并提出一种有益的方法来找出正确的趋势。在本文中，我们汇编了一个数据集，包括确诊病例数、每日死亡病例数和康复病例数。此外，通过结合随时间变化的行为和政策等病例数变量以及使用拐点的逻辑函数等机器学习算法，我们创建了新的比率，如每周死亡率、生存率以及死亡率和康复率的新方法。高斯函数表现出卓越的性能，这有助于政府提高对那些对该病毒未来趋势有重大影响的重要因素的认识。
