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.
Highlights
In December 2019, Chinese authorities released the first official information to the world about the spreading of the human coronavirus in their country as a community disease [1,2,3]
It depends on the population and took the average of total predicted cases
Once forecasting was completed, we verified whether its density mostly. It is considered as a community disease
Summary
In December 2019, Chinese authorities released the first official information to the world about the spreading of the human coronavirus in their country as a community disease [1,2,3]. Most people infected with SARS-CoV-2 are experiencing no or just mild to moderate respiratory symptoms and recover without special treatment. The COVID-19 disease causes primary symptoms such as fever, tiredness, dry cough, shortness of breath, together with other secondary symptoms such as a runny nose, sore throat, aches and pains, diarrhea, and nausea in its victims after two to fourteen days of contact. If a person touches their nose or eyes or pulls it out of their breath, there is a possibility of getting infected. Studies show that it can stay in the air for up to three hours [8], on a copper ring up to four hours, a full day on cardboard, and on plastic and stainless steel it can last up to
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