Feature: Joining the Fight

[FEATURE]

JOINING THE FIGHT

Global pandemic prompts electrical engineering and computer science faculty to take on coronavirus-related research

Lori Brandt, Anna Lynn Spitzer, Heather Ashbach, Pat Harriman

As the coronavirus outbreak continues to spread around the world, many researchers at the Samueli School of Engineering have shifted their focus to research that could help mitigate the pandemic.

Electrical engineering and computer science faculty are collaborating with other scientists on campus and around the country on projects to create tests, predict disease severity and provide better information to public health agencies.

“I’m pleased to see faculty involved in research that could be helpful in battling the global COVID-19 pandemic,” said Athina Markopoulou, professor and department chair. “Some jumped in essentially without a guarantee of funding, but because they saw the need to address a grand challenge problem and the opportunity to develop novel solutions.”

DEVELOPING ANTIBODY DETECTION METHODS

The key to getting people back to work and children back to school could lie in determining who might be immune to the virus. Those people could return to school and work, helping to slowly restart the economy. The Samueli School’s Peter Burke is working to develop an antibody test to help identify those with immunity.

Burke, professor of electrical engineering and computer science, is proposing an inexpensive point-of-care method to detect COVID-19 immunity. (Burke and colleagues at the University of Illinois recently filed for a patent.)

Antibodies in blood serum presumably can be used to determine immune patients. Ordinarily, blood tests need expensive proteins to detect antibodies produced by the immune system in response to infection, but Burke is investigating the feasibility of using short DNA sequences as the capture agent instead. He says this could be less expensive to manufacture, and could even be implemented on a low-cost paper test, like those used for pregnancy. Those who test positive for the antibodies would presumably no longer be infectious and could be cleared to return to work. Low-cost tests such as Burke’s would enable clinicians to determine how long this immunity lasts and how robust the immunity is at a global population level.

Burke says the primary goal of his study is to find and exploit DNA- or RNA-aptamers (short strands of DNA) that bind with specific affinity to the binding site of antibodies for the virus. These are known to be specific to the virus’s spike protein.

“If we are successful, then a point-of-care, in-home test of patients who are immune to COVID-19 could follow,” Burke said, adding that it could be paper-based and cost only a few cents to produce. “This would be like a pregnancy test for COVID-19-immune patients.”

PREDICTING DISEASE SEVERITY

For those infected by the coronavirus, symptoms can range from very mild to life-threatening. For hospitalized patients, Samueli School researchers are investigating the use of artificial intelligence on chest X-rays to foresee disease severity, allowing medical personnel to prioritize urgent cases by being able to predict which patients will require imminent ventilation and intensive care.

Principal investigator Dr. Arash Kheradvar, professor of biomedical engineering, is working with Hamid Jafarkhani, professor of electrical engineering and computer science, and Dr. Alpesh Amin, professor of medicine at UCI Medical Center, on the research.

“We aim to establish a cloudbased AI platform to quantify the progression of the disease during the 14 days after admission to the emergency room, based on daily chest X-rays and lab results,” said Kheradvar. “We previously have been working collaboratively on a fully automated platform for cardiac segmentation using a variety of methods involving artificial intelligence. We would like to use our expertise in designing AIbased medical imaging tools to help with mitigating the COVID-19 pandemic.”

To train the simulation models, the researchers will use COVID-19 patients’ chest X-rays taken on the first day of admission and daily for up to two weeks, in addition to pertinent clinical information and patients’ final outcomes. Accordingly, they will design a deep learning network that can predict, based on the first chest X-ray taken in the emergency room, whether a patient will develop a more severe case of the disease that may require a higher level of care.

“In the context of COVID-19, public health, emergency management entities and elected officials are on the front lines, informing and educating the public about prevention.

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IMPROVING PUBLIC HEALTH COMMUNICATIONS AND DECISION MAKING

UCI sociologist Carter Butts, an electrical engineering and computer science affiliated faculty member, is conducting two studies that could help public health officials; one relates to communicating in a crisis and the other to pandemic planning and decision making.

Disseminating information to the public during a crisis is critical for community health and safety, and Twitter, with its small character count and more than 330 million users, has become the go-to platform for public officials. With an NSF Response Research grant, Butts is studying how to craft the most effective messages for maximum reach.

“In the context of COVID-19, public health, emergency management entities and elected officials are on the front lines, informing and educating the public about prevention,” said Butts, who has spent the better part of his nearly 20-year academic career studying communication during crisis.

“This requires them to communicate directly and effectively with the public, often with little time to prepare and little vetting. In a rapidly changing environment, this is a real challenge.”

Working with Jeannette Sutton, director of the University of Kentucky’s Risk & Disaster Communication Center, he’s found that content, style and structure are the critical elements in effective messaging, and the right mix changes based on the event. The two are applying findings from past disasters to the COVID-19 pandemic. The project relies on a massive data-collection effort to capture and code all tweets posted by targeted agencies 24-hours a day, seven days a week. Those data will be used to develop models that can predict message outcomes – whether or not something will go viral or engage audiences. Butts’ goal is to develop evidence that can guide public agencies in this and future pandemics.

In another study, published in Proceedings of the National Academy of Sciences, Butts studied how population distribution impacts COVID-19 spread. He found that uneven population distribution can significantly impact the severity and timing of COVID-19 infections, leading individual communities to have vastly different experiences.

“Diseases like COVID-19 that are transmitted through intensive contact can spread very unevenly,” said co-author Butts. “Some communities get hit much earlier and harder than others, even within the same area. That can shape individuals’ understanding of infection risk, impact

their willingness to take protective actions and potentially stress health care delivery in ways that are not captured well by standard epidemiological projections.”

Using geographically detailed network models, the research team discovered significant differences in infection curves among individual census tracts due to the irregularity of social connectivity, with the disease spreading rapidly through one location but stalling at its boundaries. “While conventional diffusion models have been of considerable value ... our findings indicate that incorporating geographical heterogeneity would add value in capturing outcomes at the city or county level, which is where decisions regarding infrastructure management, health care logistics and other policies are made,” Butts said.