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Description
A mobile phone application was designed as part of an X-Prize challenge at Arizona State University (ASU). The team created an application that displays 4D visualization of time sensitive contagion data, specifically focusing on the Ebola Virus Disease. The application, named “Ebola Resource Decision Evaluator” (ERDE), is a tool to aid in resource allocation for decision-makers during epidemics and outbreaks. The predictive algorithm was based on the SIR Model—susceptible, infected, and recovered (or immune). We adapted this predictive model into our application to forecast weeks forward the Ebola incidence in three cities in the Democratic Republic of Congo (DRC).
The current 2D map used by the Center for Disease Control (CDC) displays only the number of deaths in a city caused by the outbreak. But, the cities differ in ways 2D cannot convey. We implemented the augmented reality (AR) aspect to give more meaning to data and to give decision-makers interactive 4D city-by-city comparisons. The outbreak is ongoing as of September 2019 and ASU has committed to hosting the application for other healthcare workers to use. The application incorporates the most recent data on the disease and updates to visualize how many are predicted to become infected given X units of vaccine. We are able to use the data and compare the effectiveness to other cities. After this collection of data, professionals would determine the most efficient action to take against the spread of the disease.
The current 2D map used by the Center for Disease Control (CDC) displays only the number of deaths in a city caused by the outbreak. But, the cities differ in ways 2D cannot convey. We implemented the augmented reality (AR) aspect to give more meaning to data and to give decision-makers interactive 4D city-by-city comparisons. The outbreak is ongoing as of September 2019 and ASU has committed to hosting the application for other healthcare workers to use. The application incorporates the most recent data on the disease and updates to visualize how many are predicted to become infected given X units of vaccine. We are able to use the data and compare the effectiveness to other cities. After this collection of data, professionals would determine the most efficient action to take against the spread of the disease.
ContributorsHu, Lawrence (Author) / Hall, Rick (Thesis director) / Johnson-Glenberg, Mina (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / Edson College of Nursing and Health Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05