6 Examples of Big Data Fighting the Pandemic
Big data is being used in fascinating new ways to help track COVID-19 and stop its spread.
As the COVID-19 pandemic continues to affect the world, big data experts are working hard to use their knowledge to address the most pressing matters associated with this public health crisis. Here are six fascinating examples of what's possible.
1. Finding a Vaccine
People are understandably eager to find interventions that could treat or cure the novel coronavirus. They also eagerly await the development of a potential vaccine that would let members of society take preventive action instead of being at risk of getting sick. However, making progress with those crucial aims starts with knowing which drugs might help.
Daniel Cohen, a French scientist who was the first to map the human genome in the 1980s, is now an executive at drugmaker Pharnext. He worked alongside colleagues there and identified 97 drugs that could eventually become part of a vaccine.
However, he thinks patient data sharing on a massive scale will also help things move in the right direction. "We must have a coordinated effort to mutualize COVID-19 medical records, including records of all the medications being given to patients, to analyze, with artificial intelligence tools, the influence, positive or negative, of their conditions and their drugs on this disease," Cohen noted.
2. Monitoring Hospital Bed Capacity
When the numbers of severely ill people with COVID-19 begins to rise, keeping track of available hospital beds becomes essential. Watching pandemic data and how it changes over time can signal if particular hospitals or cities may soon get overrun with patients who need care.
A collaboration between a health care brand and a geospatial data company resulted in a platform that lets people see hospital bed capacity in the United States, including available space in intensive care units.
This public-facing tool lets medical professionals—as well as concerned citizens—identify potential areas at risk for hospital overcrowding. Decision-makers can also use the data to make proactive choices that prevent worst-case scenarios from playing out.
3. Improving Relief Outreach Efforts
Government-mandated lockdowns were arguably tough for virtually everyone who experienced them. However, being in a place without reliable electricity access would undoubtedly make those uncertain periods even more daunting and unsettling.
In Lagos State, Nigeria, government officials distributed off-grid solar power systems to vulnerable communities. They knew that access to power at home would help residents in various ways, including allowing them to use devices such as mobile phones that could provide life-saving information.
The people working on this outreach project used big data to determine which communities or households would benefit most from receiving solar power equipment. This allowed them to distribute in a way that maximized the overall impact.
4. Displaying Mapped Data for Better Usability
Data mapping is a valuable way to help people make sense of information. It's particularly useful as the pandemic continues and people adjust to the new normal. For example, creating a map related to the most recent transactions helps businesses manage overlapping sales or deal with coverage and service gaps. Then, it's easier to track how the pandemic changed sales activity and adapt accordingly.
Columbia University also has a nationwide map to help analyze changing risks. People can show or deactivate various layers to see the current breakdown of pandemic data, plus what shifts would happen if several modeled scenarios come to pass.
There are even elements that show the number of older people or individuals with chronic conditions. Those specifics are important to know since comorbidities and age are two characteristics that could impact how COVID-19 affects someone.
5. Predicting Disease Outcomes
What makes some COVID-19 patients have few or no serious symptoms, and others require ventilation or have such serious effects from the virus that they ultimately prove fatal? A team used big data and artificial intelligence to shed light on why the novel coronavirus impacts people differently once they become confirmed cases.
The model examines factors related to geography, travel history, demographics and previous health when assessing what might happen to a patient. It has an accuracy rate of 94% and uses multiple algorithms.
The conclusions made by this project could prove crucial as experts make more-informed choices about how to keep people as safe as possible and identify those who are most at risk. Suppose it indicated that patients with a particular chronic illness are highly likely to die after contracting COVID-19. In that case, public health officials might recommend that individuals in that group take extra precautions.
6. Identifying Potentially Infected People
Robust contact tracing is frequently discussed as one of the key parts of keeping the virus at bay by curbing the spread. Knowing who a person interacted with and where they went before testing positive reduces instances of rampant community transmission.
Researchers took data from cruise passengers who disembarked from a vessel and engaged with Taiwanese people during a one-day tour that was part of their voyage. Big data showed 627,386 persons came in contact with the more than 3,000 ship passengers.
The team working on this big data project suggested that similar approaches could improve smart contact tracing options. They proposed that identified close contacts receive automated smartphone prompts to self-isolate, for example. Even without such notifications, this application for pandemic data helps people act quickly by showing the extent of the issue.
Big Data Aiding the Fight
Scientists are continually learning more about the novel coronavirus through their diligent efforts. These six examples show why looking at large quantities of pandemic data in purposeful ways could lead to promising outcomes and valuable lessons.