Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Noida, India.
Department of Computer Science and Engineering, G. B. Pant Government Engineering College, Okhla, New Delhi, India.
Disaster Med Public Health Prep. 2022 Jun;16(3):980-986. doi: 10.1017/dmp.2020.444. Epub 2020 Nov 18.
The coronavirus disease (COVID-19) pandemic was initiated in Wuhan Province of mainland China in December 2019 and has spread over the world. This study analyzes the effects of COVID-19 based on likely positive cases and fatality in India during and after the lockdown period from March 24, 2020, to May 24, 2020.
Python has been used as the main programming language for data analysis and forecasting using the Prophet model, a time series analysis model. The data set has been preprocessed by grouping together the days for total numbers of cases and deaths on few selected dates and removing missing values present in some states.
The Prophet model performs better in terms of precision on the real data. Prediction depicts that, during the lockdown, the total cases were rising but in a controlled manner with an accuracy of 87%. After the relaxation of lockdown rules, the predictions have shown an obstreperous situation with an accuracy of 60%.
The resilience could have been better if the lockdown with strict norms was continued without much relaxation. The situation after lockdown has been found to be uncertain as observed by the experimental study conducted in this work.
2019 年 12 月,中国大陆湖北省武汉市爆发了冠状病毒病(COVID-19)疫情,并蔓延至全球。本研究分析了 2020 年 3 月 24 日至 5 月 24 日封锁期间及之后印度 COVID-19 确诊病例和死亡人数的可能变化。
本研究使用 Python 编程语言,采用 Prophet 模型(一种时间序列分析模型)进行数据分析和预测。对数据集进行了预处理,将部分日期的总病例数和死亡数的天数分组,并删除了部分州存在的缺失值。
Prophet 模型在真实数据上的精度表现更好。预测结果显示,在封锁期间,总病例数呈上升趋势,但上升幅度得到了有效控制,准确率为 87%。在放宽封锁限制后,预测结果显示出一种不稳定的情况,准确率为 60%。
如果继续实施严格规范的封锁而不进行过多放松,恢复力可能会更好。正如本研究中的实验研究所观察到的,封锁后的情况不确定。