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1. Situational awareness of COVID pandemic data using virtual reality

   https://doi.org/10.2352/ISSN.2470-1173.2021.13.ERVR-177  

   Sharma, Sharad ; Bodempudi, Sri Teja ( March 2021 , Electronic Imaging)

   null (Ed.)

   Situational awareness provides the decision making capability to identify, process, and comprehend big data. In our approach, situational awareness is achieved by integrating and analyzing multiple aspects of data using stacked bar graphs and geographic representations of the data. We provide a data visualization tool to represent COVID pandemic data on top of the geographical information. The combination of geospatial and temporal data provides the information needed to conduct situational analysis for the COVID-19 pandemic. By providing interactivity, geographical maps can be viewed from different perspectives and offer insight into the dynamical aspects of the COVID-19 pandemic for the fifty states in the USA. We have overlaid dynamic information on top of a geographical representation in an intuitive way for decision making. We describe how modeling and simulation of data increase situational awareness, especially when coupled with immersive virtual reality interaction. This paper presents an immersive virtual reality (VR) environment and mobile environment for data visualization using Oculus Rift head-mounted display and smartphones. This work combines neural network predictions with human-centric situational awareness and data analytics to provide accurate, timely, and scientific strategies in combatting and mitigating the spread of the coronavirus pandemic. Testing and evaluation of the data visualization tool have been done with real-time feed of COVID pandemic data set for immersive environment, non-immersive environment, and mobile environment. 

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2. Real-Time Data Analytics of COVID Pandemic Using Virtual Reality

   https://doi.org/10.1007/978-3-030-77599-5_9  

   Sharma, S ; Bodempudi, S.T ; Reehl, A ( July 2021 , Lecture notes in computer science)

   Chen, J.Y.C. ; Fragomeni, G (Ed.)

   Visualizing data effectively is critical for the discovery process in the age of big data. We are exploring the use of immersive virtual reality platforms for scientific data visualization for COVID-19 pandemic. We are interested in finding ways to better understand, perceive and interact with multidimensional data in the field of cognition technology and human-computer interaction. Immersive visualization leads to a better understanding and perception of relationships in the data. This paper presents a data visualization tool for immersive data visualizations based on the Unity development platform. The data visualization tool is capable of visualizing the real-time COVID pandemic data for the fifty states in the USA. Immersion provides a better understanding of the data than traditional desktop visualization tools and leads to more human-centric situational awareness insights. This research effort aims to identify how graphical objects like charts and bar graphs depicted in Virtual Reality tools, developed in accordance with an analyst’s mental model can enhance an analyst’s situation awareness. Our results also suggest that users feel more satisfied when using immersive virtual reality data visualization tools and thus demonstrate the potential of immersive data analytics. 

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3. Immersive Virtual Reality Training Module for Active Shooter Events

   https://doi.org/10.2352/EI.2022.34.12.ERVR-299  

   Sharad Sharma, Sri Teja ( January 2022 , Proceedings of the IS&T International Symposium on Electronic Imaging (EI 2022), in the Engineering Reality of Virtual Reality)

   Active shooter events are not emergencies that can be reasonably anticipated. However, these events do occur more than we think, and there is a critical need for an effective emergency preparedness plan that can increase the likelihood of saving lives and reducing casualties in the event of an active shooting incident. There has been a major concern about the lack of tools available to allow for modeling and simulation of human behavior during emergency response training. Over the past few decades, virtual reality-based training for emergency response and decision making has been recognized as a novel alternative for disaster preparedness. This paper presents an immersive virtual reality (VR) training module for active shooter events for a building emergency response. There are two immersive active shooter modules developed: occupant’s module and Security personnel module. We have developed an immersive virtual reality training module for active shooter events using an Oculus for the course of action, visualization, and situational awareness for active shooter events. The immersive environment is implemented in Unity 3D where the user has an option to enter the environment as security personnel or as an occupant in the building. The immersive VR training module offers a unique platform for emergency response and decision making training. The platform allows for collecting data on different what-if scenarios in response to active shooter events that impact the actions of security personnel and occupants in a building. The data collected can be used to educate security personnel on how to reduce response times. Moreover, security personnel can be trained to respond to a variety of emergencies safely and securely without ever being exposed to real-world dangers. 

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4. Human-Centric Situational Awareness and Big Data Visualization

   Bodempudi, Sri Teja ; Sharma, Sharad ; Sahu, Atma ; Agrawal, Rajeev ( October 2019 , EPiC series in computing)

   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. This research effort aims to identify how graphical objects (such as data-shapes) developed in accordance with an analyst's mental model can enhance analyst's situation awareness. Our approach for improved big data visualization is two-fold, focusing on both visualization and interaction. This paper presents the developed data and graph technique based on forcedirected model graph in 3D. It is developed using Unity 3D gaming engine. Pilot testing was done with different data sets for checking the efficiency of the system in immersive environment and non-immersive environment. The application is able to handle the data successfully for the given data sets in data visualization. The currently graph can render around 200 to 300 linked nodes in real-time. 

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5. Multiple models for outbreak decision support in the face of uncertainty

   https://doi.org/10.1073/pnas.2207537120  

   Shea, Katriona ; Borchering, Rebecca K. ; Probert, William J. ; Howerton, Emily ; Bogich, Tiffany L. ; Li, Shou-Li ; van Panhuis, Willem G. ; Viboud, Cecile ; Aguás, Ricardo ; Belov, Artur A. ; et al ( May 2023 , Proceedings of the National Academy of Sciences)

   Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020. 

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