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Coronavirus disease (COVID-19) has become a global pandemic. COVID-19 patients need immediate diagnosis and rehabilitation, which makes it urgent to identify new protein markers for a prognosis of the severity and outcome of the disease. The aim of this study was to analyze the levels of interleukin-6 (IL-6) and secretory phospholipase (sPLA2) in the blood of patients regarding the severity and outcome of COVID-19 infection. The study included clinical and biochemical data obtained from 158 patients with COVID-19 treated at St. Petersburg City Hospital No. 40. A detailed clinical blood test was performed on all patients, as well as an assessment of IL-6, sPLA2, aspartate aminotransferase (AST), total protein, albumin, lactate dehydrogenase (LDH), APTT, fibrinogen, procalcitonin, D-dimer, C-reactive protein (CRB), ferritin, and glomerular filtration rate (GFR) levels. It was found that the levels of PLA2, IL-6, APTV, AST, CRP, LDH, IL-6, D-dimer, and ferritin, as well as the number of neutrophils, significantly increased in patients with mild to severe COVID-19 infections. The levels of IL-6 were positively correlated with APTT; the levels of AST, LDH, CRP, D-dimer, and ferritin; and the number of neutrophils. The increase in the level of sPLA2 was positively correlated with the levels of CRP, LDH, D-dimer, and ferritin, the number of neutrophils, and APTT, and negatively correlated with the levels of GFR and lymphocytes. High levels of IL-6 and PLA2 significantly increase the risk of a severe course by 13.7 and 2.24 times, and increase the risk of death from COVID-19 infection by 14.82 and 5.32 times, respectively. We have shown that the blood levels of sPLA2 and IL-6 increase in cases which eventually result in death and when patients are transferred to the ICU (as the severity of COVID-19 infection increases), showing that IL-6 and sPLA2 can be considered as early predictors of aggravation of COVID-19 infections. [ABSTRACT FROM AUTHOR]

"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. [ABSTRACT FROM AUTHOR]