Connecting COVID-19 with Autoimmunity

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS COV 2) is the infectious virus that causes the 2019 Coronavirus Disease (COVID-19). It is well established that SARS-CoV-2 infection can cause several autoimmune and autoinflammatory disorders. Few findings on the connection between SARS-CoV-2 and autoimmune illnesses have been published in the literature, albeit this number has been rising since 2020. SARS-CoV-2 infections and autoimmune disorders are linked in several ways. Both illnesses cause immune-mediated tissue damage, the immunological reaction is followed by an increase in inflammatory cytokine release, and both illnesses are treatable with immunomodulatory medications. Patients who suffer from certain autoimmune diseases, including systemic lupus erythematosus, rheumatoid arthritis, type 1 diabetes, cardiac sarcoidosis, idiopathic pulmonary fibrosis, autoimmune hepatitis, multiple sclerosis, and others, are more likely to develop SARS-CoV-2 infection, either because the autoimmune disease is active or because of the medications used to treat it. On the other hand, autoimmune disorders can also be brought on by SARS-CoV-2 infection. Autoimmune illnesses are chronic multifactorial disorders. The ability to identify patterns in patient data and use those patterns to forecast patient outcomes for better clinical treatment is made feasible by machine learning (ML), a subfield of artificial intelligence. Here we present a machine learning (ML) approach to run a case-control study of Autoimmunity for Candidates after COVID-19 Vaccination which can assist investigators to get an initial idea of whether COVID-19 vaccination can enhance the probability of autoimmunity occurrence. We might be able to comprehend the long-term impacts of COVID-19 vaccines better through case-control studies based on passive reporting. Passive reporting is important for giving early signals even though it is subject to bias and under- and over-reporting. By concentrating on autoimmune adverse events following immunization (AEFIs), this preliminary study adds to our understanding of the adverse effects profile of the COVID-19 vaccine. It shows that, for the majority of potential AEFIs, an association with a COVID-19 vaccination is significantly less likely than an association with a non-COVID-19 vaccine.