Using Artificial Intelligence to Tackle COVID-19

As difficult as it may be to believe, the U.S. first issued an emergency declaration in response to COVID-19 on March 13, 2020 – nearly two and a half years ago. 

The ongoing pandemic continues to draw new attention to the unique challenges public health officials face in a global society, whereby diseases that once seemed confined by geographic barriers have new opportunities to spread worldwide – and do so quickly. The SARS-CoV-2 virus continues to evolve and new variants have contributed to the rise and fall in case numbers, creating new challenges for global leaders, as well as for those in health care and at the front lines of the disease. 

Behind the scenes, epidemiologists carry out detective work to better understand how the disease spreads, who it may impact most, and how the degree of severity may change.

Central to this understanding is data – a whole lot of it. 

That’s where biocomputational engineers have a unique ability to play a major role in advancing understanding of the impacts of diseases – like SARS-CoV-2 – and how a disease spreads in a given population, taking into account different environmental factors. To translate this data from numbers into meaningful information that could be used to predict how disease cases are likely to increase or how a disease might change over time, biocomputational engineers and others working at the nexus of computer science and biology have a unique weapon in their arsenal: artificial intelligence (AI).

Jarred Callura, a University of Maryland biocomputational engineering faculty member, explained how Brian Hie, a computational biologist at MIT, took an AI program designed to analyze patterns in human language and reworked it to analyze patterns in viral sequences. He also explained that AI and machine learning can be used for discovering and optimizing antiviral drugs and vaccines for fighting COVID-19. Using computational models, engineers can work with the structure of proteins encoded by a virus in order to predict inhibitor molecule candidates or identify vaccine targets.

For example, researchers at Baylor College of Medicine and Amity University in Noida, India have used AI to accelerate the vaccine development process. They created a platform to find important vaccine targets and epitopes that could transform the vaccine discovery process for COVID-19. The research group successfully tested their platform on 40 different pathogens, including SARS-CoV-2 and other deadly diseases. 

The coronavirus pandemic highlights an ongoing – and urgent – need for experts who can work with big data to help advise public health leaders, epidemiologists, and even vaccine engineers. 

One of the most direct pathways to working in this field runs through the University of Maryland’s B.S. in Biocomputational Engineering degree program. Here, biocomputational engineering students conduct research at the nexus of biology, data science, and computation, honing skills in machine learning, Python, C++, and R, as well as in synthetic biology, molecular biology, mathematics, and statistics.

This unique combination of skills and expertise will equip biocomputational engineering graduates with the knowledge base and hands-on experiences needed to impact biopharma, biotech, and biomedical industries for decades to come.

Are you interested in learning more about how biocomputational engineers can advance our understanding of diseases? Attend an upcoming Summer Info session – either in person or virtually – to learn more about what you can do with a bachelor's degree in Biocomputational Engineering!


Return to all stories