一种基于统计和深度学习的新冠疫情每日感染人数预测系统。
A statistical and deep learning-based daily infected count prediction system for the coronavirus pandemic.
作者信息
Shah Vruddhi, Shelke Ankita, Parab Mamata, Shah Jainam, Mehendale Ninad
机构信息
K. J. Somaiya College of Engineering, Mumbai, 400077 Maharashtra India.
出版信息
Evol Intell. 2022;15(3):1947-1957. doi: 10.1007/s12065-021-00600-2. Epub 2021 Apr 3.
UNLABELLED
We present new data analytics-based predictions results that can help governments to plan their future actions and also help medical services to be better prepared for the future. Our system can predict new corona cases with 99.82% accuracy using susceptible infected recovered (SIR) model. We have predicted the results of new COVID cases per day for dense and highly populated country i.e. India. We found that traditional statistical methods will not work efficiently as they do not consider the limited population in a particular country. Using the data analytics-based curve we predicted four most likely possibilities for the number of new cases in India. Hence, we expect that the results mentioned in the manuscript help people to better understand the progress of this disease.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s12065-021-00600-2.
未标注
我们展示了基于新数据分析的预测结果，这些结果可以帮助政府规划未来行动，也有助于医疗服务为未来做好更好的准备。我们的系统使用易感-感染-康复（SIR）模型预测新冠新病例的准确率可达99.82%。我们已经预测了人口密集和高度密集的国家（即印度）每日新冠新病例的结果。我们发现传统统计方法无法有效发挥作用，因为它们没有考虑到特定国家的有限人口。利用基于数据分析的曲线，我们预测了印度新病例数的四种最有可能的情况。因此，我们期望手稿中提到的结果能帮助人们更好地了解这种疾病的进展。
补充信息
在线版本包含可在10.1007/s12065-021-00600-2获取的补充材料。
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