Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death
Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death
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DOI：
10.3390/s20113089
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发表时间：
2020-06-01
期刊：
影响因子：
3.9
通讯作者：
Martinez, Santiago G.
中科院分区：
文献类型：
--
作者：
Chatterjee, Ayan;Gerdes, Martin W.;Martinez, Santiago G.
新型冠状病毒“严重急性呼吸综合征冠状病毒2 （SARS-CoV-2）”是目前全球大流行的罪魁祸首。“世界卫生组织（WHO）”指定了“国际疾病分类（ICD）”代码-“COVID-19”作为新疾病的名称。冠状病毒通常由人和许多不同种类的动物传播，包括鸟类和哺乳动物，如牛、骆驼、猫和蝙蝠。罕见情况下，冠状病毒可以从动物转移到人类，然后在人与人之间传播，例如“中东呼吸综合征（MERS-CoV）”，“严重急性呼吸综合征（SARS-CoV）”，以及现在的这种新病毒，即“SARS-CoV-2”，或人类冠状病毒。随着医疗服务机构难以应对，其迅速蔓延已使数十亿人陷入封锁状态。2019冠状病毒病爆发之际，新感染病例呈指数级增长，死亡人数也在不断增加。限制指数扩展的一个主要目标是降低传输速率，用“扩展因子(f)”表示，本研究提出了一种算法来分析该算法。本文分析了“ourworldindata.org（牛津大学数据库）”中的现有数据集，并分析了用于预测新病例和由此导致的死亡的不同单变量“长短期记忆（LSTM）”模型，探讨了数据科学在评估与COVID-19相关的风险因素方面的潜力。结果表明，香草、堆叠和双向LSTM模型优于多层LSTM模型。此外，我们还讨论了与模拟数据集统计分析相关的研究结果。为了进行相关性分析，我们纳入了过去三个月（2020年1月、2月和3月）的外部温度、降雨量、日照、人口、感染病例、死亡、国家、人口、地区和人口密度等特征。对于使用LSTM的单变量时间序列预测，我们使用了2020年1月1日至2020年4月22日的数据集。
"Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", the novel coronavirus, is responsible for the ongoing worldwide pandemic. "World Health Organization (WHO)" assigned an "International Classification of Diseases (ICD)" code-"COVID-19"-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with "Middle East Respiratory Syndrome (MERS-CoV)", "Severe Acute Respiratory Syndrome (SARS-CoV)", and now with this new virus, namely "SARS-CoV-2", or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a "spread factor (f)", and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in "ourworldindata.org (Oxford University database)", and newly simulated datasets, following the analysis of different univariate "Long Short Term Memory (LSTM)" models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months-January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.