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Highlights Lethal forms of the disease are associated with cytokine storm. The severity of COVID-19 was associated with abdominal adipose tissue distribution. Exhaustion markers upregulated in T cells and NK cells from COVID-19 patients. There might be a connection between SARS-CoV-2 related mortality and gut microbiota.

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Coronavirus Disease 2019 caused by the SARS-CoV-2 virus is spreading rapidly worldwide and has led to widespread deaths globally. As a result, the early diagnosis of patients with COVID-19 is vital to control this dangerous virus's release. There are two common diagnosing methods, chest computed tomography scan (CT-scan) and Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. The most significant disadvantages of RT-PCR molecular tests are the high cost and the long waiting time for test results. The common weaknesses of chest CT-scan are the need for a radiologist to analyze, a misdiagnosis of flu disease due to its similarity, and risky for pregnancy and infants. This article presents a low-cost, highly available method for early detection of COVID-19 based on Artificial Intelligence (AI) systems and blood tests. In this study, 6635 patient's blood tests are used. Experiments conducted using three machine learning algorithms. The results show that the proposed method can detect COVID-19 with an accuracy of %84 and an F1-score of %83. The trained model is being used in a real-world product through an online website called CODAS.