Data Analysis Helps Create COVID-19 Risk Assessment App
Researchers used data analysis to develop a COVID-19 risk assessment to limit possible exposure to the virus.
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By - Through data analysis, University of Houston researchers created a real-time COVID-19 infection risk assessment and mitigation (RT-CIRAM) system.
With the high transmission rates of COVID-19, individuals, especially those at high risk of the disease, may wonder when they should go out and run an errand to avoid possible exposure.
Using data insights, the University of Houston’s mobile phone system app can identify the best times for individuals to avoid large crowds to minimize COVID-19 infection risk.
“Preliminary work has been performed to determine the usability of a number of COVID-19 data websites and other websites such as grocery stores and restaurants’ popular times and traffic,” professor of computer science and electrical and computer engineering at the University of Houston, Albert Cheng, said in a press release.
“Other data, such as vaccination rates and cultural factors (for example, the percentage of people willing to wear facial coverings or masks in an area), are also used to determine the best grocery store to shop in within a time frame.”
Using the COVID-19 infection risk assessment app, users give input values for the intended destination(s), the farthest distance they will travel (in a personal car or public transport), and the time frame (start time and deadline).
The app will then provide information on how to get to the target location in the user’s timeframe to reduce the risk of COVID-19 infection.
“We are leveraging urgent high-performance cloud computing, coupled with time-critical scheduling and routing techniques, along with our expertise in real-time embedded systems and cyber-physical systems, machine learning, medical devices, real-time knowledge/rule-based decision systems, formal verification, functional reactive systems, virtualization, and intrusion detection,” said Cheng.
While mobile phone COVID-19 contract tracing and proximity apps already exist, according to Cheng, they are only effective when the majority of mobile phone users adhere to the procedures required by the apps.
“Even in countries like Singapore where stringent nonpharmaceutical interventions (NPIs) can be implemented, these apps have not been shown effective and often provide a false sense of safety from infection for the mobile phone users,” Cheng continued.
“This personal system will be useful for an individual citizen to reduce her/his infection risk while contributing to containing the spread of the COVID-19 and future pandemics.”
Cheng presented the app at the Institute of Electrical and Electronics Engineers (IEEE) conference HPC (High Performance Computing) for Urgent Decision Making and will publish the work in IEEE Xplore.
