Case Series of New-Onset CNS Immunological Disorders Temporally Associated with mRNAbased COVID-19 Vaccines

Objective: To present a single-health system retrospective analysis of post-mRNA-based COVID-19 vaccination CNS autoimmunity conducted in the greater New York City area. Background: There have been rare reports associating mRNA-based COVID-19 vaccines with central nervous system (CNS) inflammation. We report a case series of five patients with newonset neurological disorders of immunological origin temporally associated with these vaccines. Design/Methods: Case-series. Results: Five cases of post-vaccination CNS disorders of immune origin were observed within two weeks of inoculation with either the first or second dose of mRNA-based COVID-19 vaccines (Moderna = 3, Pfizer = 2). This includes: Fatal ADEM (n = 1), new-onset NMO (n = 2), new-onset fulminant MS (n = 1), and meningoencephalitis (n = 1). The age of our patients ranged from 27 to 81, and three were female. None of the patients had pre-existing neurological illnesses and one had a pre-existing autoimmune condition (immune thrombocytopenia purpura). New-onset focal neurological symptoms were present in all five patients, including quadriparesis, numbness, diplopia, and encephalopathy. CSF pleocytosis was present in all patients, and three had elevated protein. All but one patient (meningoencephalitis) had contrastenhancing lesions involving either the cerebrum or spinal cord. Both NMO patients had longitudinally extensive transverse lesions involving the central thoracic cord. Aquaporin-4 serum antibody was present in one NMO patients and aquaporin-4 CSF antibody present in the other. All but one patient (fatal ADEM) clinically improved with pulse steroids or plasmapheresis. Conclusions: These are among the emerging cases of CNS immunological events temporally associated with mRNA-based COVID-19 vaccines. These findings should be interpreted with great caution as they neither prove a link nor imply a potential long-term increased risk in postvaccination CNS autoimmunity. Larger prospective studies are needed. The mRNA-based SARS-CoV-2 vaccines should continue to be strongly encouraged given their high efficacy in overcoming this pandemic.

COVID19(alpha) : Interactive Spatio-Temporal Visualization of COVID-19 Symptoms through Tweet Analysis

In this demo, we focus on analyzing COVID-19 related symptoms across the globe reported through tweets by building an interactive spatio-temporal visualization tool, i.e., COVID19(alpha). Using around 462 million tweets collected over a span of six months, COVID19(alpha) provides three different types of visualization tools: 1) Spatial Visualization with a focus on visualizing COVID-19 symptoms across different geographic locations;2) Temporal Visualization with a focus on visualizing the evolution of COVID-19 symptoms over time for a particular geographic location;and 3) Spatio-Temporal Visualization with a focus on combining both spatial and temporal analysis to provide comparative visualizations between two (or more) symptoms across time and space. We believe that health professionals, scientists, and policymakers will be able to leverage this interactive tool to devise better and targeted health intervention policies. Our developed interactive visualization tool is publicly available at https://bijoy-sust.github.io/Covid19/.

Face detection based approach to combat with COVID-19

The global problem at the moment is COVID-19 caused by corona-virus which led to the worldwide lockdown. However there are still people who are not taking proper precautions and maintaining social-distancing while going out for emergency situations. To ensure this the government is trying to monitor the actions of each and every citizen. It is not realistically possible to monitor the actions of each and every citizen. This problem can be possibly solved by the use of machine learning. In this paper we have presented face detection based technique to combat with COVID-19. At first this technique captures the image and detects the face using viola zones method. Then the detected face images are classified to detect the mask. It actually detects whether every person have used mask or not. If there present anyone without mask then the system starts alarm to inform it to the responsible authority and to alert the public. Here Principal Component Analysis (PCA) has been used to extract the feature from the detected face images and for the detection of mask K-Nearest Neighbor (KNN) classifier has been used. © 2021 Institute of Physics Publishing. All rights reserved.