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In a world ravaged by COVID-19, having access to the right data is literally a matter of life or death. Governments need authentic data for tracing the possible contacts of an infected person and identifying potential clusters. But that information gets accumulated on a day-to-day basis and can only help in slowing down the spread. In the long run, world leaders need to make big decisions backed by big data.
Big data gives insights into preventive action, illustrates the resilience of people and systems to combat the virus, and gives an idea about the population mobility of a country. These are all vital in mapping out a long-term strategy to stop coronavirus. Countries that have used big data have been able to sustain the outbreak so far.
How Big Data has Helped so Far
Here are some solutions big data has offered to combat the scourge of coronavirus.
1. Integration of Large Databases to Track Down Possible Infected
Taiwan has been extremely successful in curtailing the COVID-19 outbreak. Most of it is owed to the timely implementation of big data analytics according to a recently published report in the Journal of the American Medical Association (JAMA).
The Taiwanese officials were able to map out the person-to-person transfer and stop a good number of transmissions in the early stages of the outbreak. This was made possible by integrating Taiwan’s national health insurance database with the country’s immigration and customs database.
Armed with this information, the government was able to track 14-day travel histories and possible symptoms in its citizens. All of these citizens had National Health Insurance (NHI) cards. Clinics, pharmacies, and hospitals across the country were given this information for every patient.
This approach allowed the government to avoid a nationwide lockdown and other drastic measures. As of March 4th, the number of infected people in Taiwan is 363, and the country has only lost 5 citizens to the novel coronavirus.
2. Monitoring Movement of the Population Through Mobile Data
Dalberg Data Insights, an organization tasked by the Belgium Government to assist its efforts against coronavirus, has examined anonymized and aggregated telecom data from three of the major telecom operators in the country.
The company’s main goal was to analyze human mobility trends related to lockdown measures and identify the risk of infections in a specific region. It found that overall in Belgium, human mobility declined with an average of 54 percent — some areas had bigger decreases than others.
This whole operation has allowed crisis response teams to have a dataset to refer to while analyzing the impact of measures and indicating risks of outbreaks.
Likewise, South Korea has monitored quarantined citizens through a mobile app. The app allows people to communicate with the local government and report their symptoms. Both the citizen and government are notified when they leave the designated quarantine zone.
South Korea has been touted as an example of how countries can combat COVID-19. Their data-backed approach has proven to be potent against the pandemic.
3. Making Clinical Trials More Effective
Clinicals trials operations gain great benefits from big data to connect patients with healthcare providers. This creates an environment where site and patient recruitment is faster and more targeted.
But the possibilities are endless. Parexal is exploring methods to create large-scale COVID-19 studies that accumulate data from what’s been observed with the patients currently in treatment.
This will allow healthcare professionals to make faster and informed decisions based on accurate data. Doing so would save many lives.
Likewise, another idea under consideration is to identify patients at the point of testing and accumulate patient histories and track progress. This can be achieved by linking through tokenization by limiting the burden on the patient and site.
4. Identifying Vulnerable Communities
When it comes to stopping coronavirus, identifying vulnerable communities is crucial. It can lead to preventive measures, infrastructure improvement, and emergency funding in underdeveloped localities.
Underprivileged communities are at high-risk because people there live in high-density areas, lack running water for washing hands, and mostly rely on daily wages. Governments can identify these areas within the population by using primary data collection, satellite images, and statistics from national bureaus.
Already, Location Analytics (LOCAN) in Kenya is examining high-risk profiles in countries across Africa. Their results are fed into epidemiological models and help in informed decisions. In Nigeria, a similar risk model identifies three factors — people over 60, individuals who use dirty cooking fuel at home, and regular smokers — to determine which people are vulnerable to COVID-19.
Beyond Coronavirus: Will Big Data Help Economies Recover?
Lockdowns caused by coronavirus spread have devastated economies around the world. Millions have become unemployed while thousands of businesses are on the brink of bankruptcy. For the world to recover, big data could be extremely crucial.
Authorities could use the data of everyone who lost their jobs due to the lockdowns and compensate them so that they can get their lives back on track. Similarly, data on sectors that have been hit the hardest by the pandemic could help in crafting policies for their regrowth.
Informed, data-backed decisions can help the world’s economy get back to its previous state and move forward in a post-coronavirus world.