The goal of VAPOC (Visualization, Analysis and Prediction of COVID-19) project is to find out reasons as to why the black community is disproportionally impacted during the coronavirus pandemic. The VAPOC project combines neural networks predictions with human centric situational awareness and data analytics to provide accurate, timely and scientific strategy in combatting and mitigating the spread of the coronavirus plague in the black community. We believe that a combination of factors is responsible for the African Americans' susceptibility to the COVID-19. They hypothesize that pre-existing conditions, type of employment, and access to healthcare among other factors have significant influence in the higher death rate of African Americans during the COVID-19 pandemic. Their study is based on data visualization, pattern recognition, knowledge discovery, and scientific principles. To accomplish the above goal, the following 3 objectives are proposed;
1) Design, develop and evaluate a COVID-19 model to determine vulnerability to coronavirus
2) Development of a visualization and interaction tool to analyze COVID-19 patients' dataset in an immersive, non-immersive environment, and mobile environment to enhance situational awareness
3) Design, develop and evaluate a deep learning model to predict extent of COVID-19 damage to discharged patients.
Human-centric situational awareness and visualization are needed for analyzing the big data in an efficient way. One of the challenges is to create an algorithm to analyze the given data without any help of other data analyzing tools. Our approach for improved big data visualization is two-fold, focusing on both visualization and interaction.
Data Visualization of of COVID-19 Pandemic in USA
Data Visualization of COVID-19 Pandemic (Oculus Version)
Data Visualization of COVID-19 Pandemic (Mobile Version)
Version 2: Data Visualization and Data Analytics (Non-Immersive VR)
Version 1: Data Visualization using Force directed graph (Non-Immersive VR)
User grabing the data node using oculus rift touch controllers (Immersive VR)
User grabing the data node using oculus rift touch controllers
Publications
Sharma, S, Bodempudi S.T., Reehl A., "Real-Time Data Analytics of COVID Pandemic Using Virtual Reality", In: Chen J.Y.C., Fragomeni G. (eds) Virtual, Augmented and Mixed Reality. HCII 2021. Lecture Notes in Computer Science, vol 12770. Springer, Cham, https://doi.org/10.1007/978-3-030-77599-5_9, 2021.
Walker, T, Sharma, S.," Data Analysis of Crime and Rates of Hospitalization due to COVID-19, Proceeding of the IEEE International Conference on Computational Science and Computational Intelligence, (CSCI'21),Symposium of Big Data and Data Science (CSCI-ISBD), Las Vegas, USA, December 15-17, 2021.
Walker, S., Sharma, S.," Data Visualization of Covid-19 and Crime Data, Proceeding of the IEEE International Conference on Computational Science and Computational Intelligence, (CSCI'21), Symposium of Big Data and Data Science (CSCI-ISBD), Las Vegas, USA, December 15-17, 2021.
Sharma, S, Bodempudi, S.T., Reehl, A. " Real-Time Data Visualization to Enhance Situational Awareness of COVID pandemic ", Proceeding of the IEEE International Conference on Computational Science and Computational Intelligence (CSCI'20), Las Vegas, Nevada, USA, Dec 16-18, 2020.
Rayan, T., Brown,A., Carillo, A., Sharma, S, " The Effect of COVID-19 on Various Racial Demograpics in the United States ", Proceeding of the IEEE International Conference on Computational Science and Computational Intelligence (CSCI'20), Las Vegas, Nevada, USA, Dec 16-18, 2020.
