How Data Science Is Being Used to Understand COVID-19

Read this overview to gain an understanding of how data scientists are working hard to learn as much about COVID-19 as they can.

By Kayla Matthews, Productivity Bytes on April 10, 2020 in Coronavirus, Data Science, Healthcare

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Source: United Nations

The rapid spread and global impacts of COVID-19 can make people feel helpless and scared as the novel coronavirus escalates and forces them to change many aspects of their everyday lives.

However, people can feel glimmers of hope in these uncertain times by understanding more about how data scientists are working hard to learn as much about COVID-19 as they can.

Data Science Can Give Accurate Pictures of Coronavirus Outcomes

Medical professionals and others must get correct and up-to-date information about how the coronavirus situation changes day by day. Several organizations, including Johns Hopkins University, IBM and Tableau, have released interactive databases that offer real-time views of what's happening with the virus.

Many of these sources pull from data provided by trusted bodies such as the U.S Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO). They also include direct links to those places so that people have quick, easy access to reliable information.

Using these databases can inform people of the number of confirmed cases, fatalities and recoveries. Then, whether a person is on the front lines of the coronavirus fight or a concerned citizen trying to stay informed, they can get all or most of the information they need in one place.

Data Scientists Devise a Speedier Way to Handle Contact Tracing

Contact tracing is an effective way to slow COVID-19. It involves getting in touch with a person's close contacts after that individual tests positive for the virus and telling them to self-isolate. Contact tracing is time-consuming, although it's getting easier as more people take social distancing seriously.

Data scientists and medical experts teamed up at Oxford University to make contact tracing even more efficient. The experts working on the project asserted that mathematical models showed them how traditional methods of contact tracing used in public health are not fast enough to thoroughly slow the spread of COVID-19.

They created a mobile phone-based solution to eliminate the need for people to call the contacts manually. Instead, those parties get text messages confirming the need for self-isolation. The researchers clarify that their approach would be most effective if it gets support from national leaders and is not an effort primarily spearheaded by independent app developers.

No nations are using this method yet. Given the market penetration of mobile phones and the familiarity people have with receiving texts, however, it's easy to see why this approach makes sense.

Everyone Can Play a Part in Helping Scientists Fight the Coronavirus

Many people with COVID-19 have only mild symptoms or none at all. Plus, the classic symptoms include a fever and a cough — two issues not restricted to the coronavirus. These things could make it easier for people to unknowingly spread the disease. But, developers created an app that uses data-sharing to help medical experts learn more about the virus.

It's called the Covid Symptom Tracker and already has at least 200,000 users. People can and should interact with the app even if they are asymptomatic or do not think their symptoms are COVID-19-related. The more researchers know about the coronavirus, the better equipped they are to tackle it.

People interact with the app to do a short daily symptom check-in. They also give their age and postal code, plus disclose any preexisting conditions. That information helps scientists determine the groups that are most affected or at risk. The app does not take user data for commercial purposes, but it gives it to people who are working to stop the coronavirus, including some at health organizations.

Data Scientists Use Machine Learning to Find Possible Cures Faster

Besides the race to restrict the COVID-19 spread, scientists are working as quickly as possible to uncover effective treatments. Two graduates of the data science program at Columbia University have turned to machine learning to help. The typical process of antibody discovery in a lab takes years. This approach, however, takes only a week to screen for therapeutic antibodies with a high likelihood of success.

The team taking this approach says this method is less costly than traditional ones, too. Humans are still part of the process because they have to test the gene sequences identified as most promising by the machine learning algorithm. However, using this expedited method could be crucial in efficiently finding interventions that work for coronavirus patients.

Data Science Can Help Track the Spread

Data science specialists have also concluded that graph databases are instrumental in showing them how COVID-19 spreads. A graph database shows links between people, places or things. Scientists refer to each of those entities as a node, and the connections between them are the "edges." The results give a visual representation of the relationship between things, if any.

In the early days of the coronavirus outbreak, Chinese data scientists built a graph database tool called Epidemic Spread. It allowed people to type in identifying information associated with the journeys they took, such as a flight number or even a car's license plate. The database would then tell those users whether anyone with a confirmed coronavirus case took those same trips and may have spread it to fellow passengers.

Making Progress Against COVID-19

Knowing as much about the coronavirus as possible will save lives. These are only some of the fascinating ways that data scientists are using their skills to help.

Bio: Kayla Matthews discusses technology and big data on publications like The Week, The Data Center Journal and VentureBeat, and has been writing for more than five years. To read more posts from Kayla, subscribe to her blog Productivity Bytes.

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