The Fight Against COVID-19 Continues

“There's a potentially high impact when you're trying to reach the people who are either lacking information, time or resources to take action on their own behalf,” Dr. Oren says. He is also working with San Diego County and state officials to conduct public health surveillance on who is likely to contract the virus, and to establish best strategies for communicating virus information to vulnerable communities as part of a larger Centers for Disease Control and Prevention initiative.

“We have an overarching goal of building a connection with the community, which we can then leverage to promote the vaccination efforts once those are ready,” Dr. Kiene says.

But the teams are also providing other resources to community members, including food provision, rental assistance, guidance on self-isolation and counseling for people who test positive.

“We're putting a lot of effort into those types of resources your traditional testing sites might not have,” Kiene says. “We recognize they are crucial in making sure people have the support they need to protect themselves and their families.”

California State University, Fullerton

Project: Mathematical and statistical models that predict the spread of COVID-19 through California counties and determine actions that diminish contraction.

How it helps: These models are products of a coordinated effort by Center for Computational and Applied Mathematics​ (CCAM) faculty Derdei Bichara, Ph.D., assistant professor of mathematics, whose research focuses on mathematical modeling of infectious diseases, and Sam Behseta, Ph.D., statistician, mathematics professor and CCAM director—along with their student teams.

“The mathematical framework allows us to see things from a theoretical standpoint, but the statistical standpoint also gives us a lot of empirical observations,” Dr. Bichara says. “We have a good set of data for COVID-19, so we want to use that to see how our prediction from the theoretical standpoint matches with the trend from the statistical models.”

When developing his mathematical and machine learning models, Bichara incorporates factors that affect the disease’s spread, such as the latency period during which individuals are contagious, travel and interactions outside the home, public health orders and personal behaviors like mask wearing and hand washing. His models can be updated as people’s behaviors and public health measures change. “We want to study and incorporate not only the disease itself, but also the human behavioral aspect of the phenomenon,” Bichara says.

For the statistical models, Dr. Behseta analyzed current data around people’s travel (from Apple or Google, for example), COVID-19 case numbers and geography, determining which factors would contribute to higher case rates. His main findings showed economically challenged counties saw higher case numbers due to their residents’ need to leave the home, Behseta explains.

“If everybody in the ideal world could sit at home and not interact with anybody else, the disease would not spread as fast,” he says. “But people have to work. People have to go to the grocery store. And while there are shelter-in-place and other policies that local and federal governments can impose upon the citizens, unfortunately not everybody's going to abide by that.”

Ultimately, these models can help public health officials make decisions around restrictions to effectively prevent the further spread of COVID-19.

“Our hope is to give an unbiased study of how this pandemic is evolving and particularly how to mitigate it,” Bichara says. “And we hope it gives the general public and the policy makers a glimpse of how much mathematics could also help combat against this disease.”

California State University, East Bay

Project: COVID ID, a mobile phone application that detects four safety factors in public spaces—mask wearing, crowd density, social distancing and fever indicators—using computer vision and machine learning.

How it helps: Currently an open source application for Android phones, the app helps individuals protect themselves from the virus as they head out into public.

“The idea behind it is … health situation awareness, understanding your environment around you,” says Lynne Grewe, Ph.D., professor of computer sci​ence who led the student development team. “The common person can decide where they want to go or, within even an area, if it's safe to go into a particular area.”

Individuals already in public can scan their environment with the app to determine the safest spaces around them based on those four factors, for example choosing which line to wait in at the grocery store. However, detecting instances of fever does require an infrared camera that connects to the mobile device. In addition, the app’s map interface collects the real-time information so other users can see how safe a location is before they arrive.

The COVID ID app helps the user visualize the health safety of their surroundings by analyzing mask wearing, crowd density, social distancing and fever.

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“There's also a tracking module that'll let you visualize the area around you,” explains recent bachelor’s graduate Emmanuel Gallegos. “So, somebody could be walking through campus and say, ‘Oh, I'm going to go this way because farther up ahead there's a crowd of people.’”

Under Dr. Grewe’s direction, the app was developed by CSUEB graduate students Subhangi Asati, Shivali Choudhary, Divya Gupta, Maithri House, Cemil Kes, Buhmit Patel, Kunjkumar Patel, Dikshant Pravin Jain and Manasi Rajiv Weginwar; CSUEB undergraduates Emanuel Gallegos and Jamie Ngyuen; California State University, Dominguez Hills undergrad Phillip Aguilera; Santa Clara University undergrad Allen Shahshahani and high school student Jake Shahshahani.