About: Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death

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About: Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death Goto Sponge NotDistinct Permalink

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death
Creator	Chatterjee, Ayan Gerdes, Martin Martinez, Santiago
Source	Medline; PMC
abstract	“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.
has issue date	2020-05-29(xsd:dateTime)
bibo:doi	10.3390/s20113089
bibo:pmid	32486055
has license	cc-by
sha1sum (hex)	f18b14f8baa39c817258364312957cd2645b4a79
schema:url	https://doi.org/10.3390/s20113089
resource representing a document's title	Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death
has PubMed Central identifier	PMC7308840
has PubMed identifier	32486055
schema:publication	Sensors (Basel)
resource representing a document's body	covid:f18b14f8baa39c817258364312957cd2645b4a79#body_text
is schema:about of	named entity 'LSTM' named entity 'pneumonia' named entity 'timeseries' named entity 'SARS-CoV-2' named entity 'incubation period' named entity 'hypertension' named entity 'risk factors' named entity 'COPD' named entity 'spreading factor' named entity 'unit root' named entity 'Algorithm' named entity 'LSTM' named entity 'algorithm' named entity 'MAE' named entity 'France' named entity 'Springer' named entity 'CSSE' named entity 'long short-term memory' named entity 'epidemiologists' named entity 'null hypothesis' named entity 'social distancing' named entity 'health and wellness' named entity 'COVID-19' named entity 'LSTM' named entity 'COVID-19' named entity 'population density' named entity 'Italy' named entity 'COVID-19' named entity 'Population Density' named entity 'COVID-19' named entity 'developed nations' named entity 'vaccination' named entity 'social isolation' named entity 'ICU' named entity 'open access' named entity 'timeseries' named entity 'model evaluation' named entity 'human coronavirus' named entity 'LSTM' named entity 'PEER REVIEW' named entity '2019-nCoV' named entity 'iPads' named entity 'timeseries' named entity 'LSTM' named entity 'regression problem' named entity 'model training' named entity 'flattening the curve' named entity 'total death' named entity 'Figure 8' named entity 'RNN' named entity 'non-stationary' named entity 'Netherlands' named entity 'forecast error' named entity 'social distancing' named entity 'LSTM' named entity 'statistical analysis' named entity 'quarantine' named entity 'COVID-19' named entity 'medical conditions' named entity 'scientific community' named entity 'guesstimate' named entity 'sequence database' named entity 'LSTM' named entity 'statistically significant' named entity 'nitrogen dioxide' named entity 'feedforward neural networks' named entity 'Springer' named entity 'sensor' named entity 'total death' named entity 'linear relationship' named entity 'Germany' named entity 'epidemic' named entity 'data collection' named entity 'WHO' named entity 'India' named entity 'LSTM' named entity 'LSTM' named entity 'clinical data' named entity 'Data preprocessing'

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