Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death
1
Department of Information and Communication Technology, Centre for e-Health, University of Agder, 4604 Kristiansand, Norway
2
Department of Health and Nursing Science, Centre for e-Health, University of Agder, 4604 Kristiansand, Norway
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(11), 3089; https://doi.org/10.3390/s20113089
Received: 25 April 2020
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Revised: 25 May 2020
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Accepted: 27 May 2020
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Published: 29 May 2020
(This article belongs to the Section Biomedical Sensors)
“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.
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Keywords:
COVID-19; public health; ICD; community disease; transmission rate; RNN; LSTM; spread factor; population; correlation; regression; python; hypothesis test; keras; measurable sensor data; artificial intelligence; deep learning; machine learning; statistics; algorithm
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MDPI and ACS Style
Chatterjee, A.; Gerdes, M.W.; Martinez, S.G. Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death. Sensors 2020, 20, 3089. https://doi.org/10.3390/s20113089
AMA Style
Chatterjee A, Gerdes MW, Martinez SG. Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death. Sensors. 2020; 20(11):3089. https://doi.org/10.3390/s20113089
Chicago/Turabian StyleChatterjee, Ayan, Martin W. Gerdes, and Santiago G. Martinez. 2020. "Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death" Sensors 20, no. 11: 3089. https://doi.org/10.3390/s20113089
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