新型冠状病毒肺炎（COVID-19）传播轨迹的分析与预测：一种机器学习方法。
Analysis and prediction of COVID-19 trajectory: A machine learning approach.
作者信息
Majhi Ritanjali, Thangeda Rahul, Sugasi Renu Prasad, Kumar Niraj
机构信息
School of Management National Institute of Technology Karnataka Surathkal Mangalore Karnataka.
National Institute of Technology Warangal India.
出版信息
J Public Aff. 2021 Nov;21(4):e2537. doi: 10.1002/pa.2537. Epub 2020 Nov 18.
Abstract
The outbreak of Coronavirus 2019 (COVID-19) has impacted everyday lives globally. The number of positive cases is growing and India is now one of the most affected countries. This paper builds predictive models that can predict the number of positive cases with higher accuracy. Regression-based, Decision tree-based, and Random forest-based models have been built on the data from China and are validated on India's sample. The model is found to be effective and will be able to predict the positive number of cases in the future with minimal error. The developed machine learning model can work in real-time and can effectively predict the number of positive cases. Key measures and suggestions have been put forward considering the effect of lockdown.
摘要
2019年冠状病毒病（COVID-19）疫情已对全球日常生活产生影响。阳性病例数量不断增加，印度现已成为受影响最严重的国家之一。本文构建了能够更准确预测阳性病例数量的预测模型。基于回归、决策树和随机森林的模型已根据中国的数据构建，并在印度的样本上进行了验证。该模型被发现是有效的，并且能够在未来以最小的误差预测阳性病例数量。所开发的机器学习模型可以实时运行，并能有效预测阳性病例数量。考虑到封锁的影响，还提出了关键措施和建议。
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2
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Chaos Solitons Fractals. 2020 May;134:109761. doi: 10.1016/j.chaos.2020.109761. Epub 2020 Mar 21.
3
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4
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