The World Health Organization (WHO) declared COVID-19 as a pandemic on March 11, 2020, but data scientists, analysts, epidemiologists, virologists and many others have been monitoring the spread of this coronavirus for much longer. Nations across the globe are supposed to report their data to WHO. While info sharing has not been as robust as would be ideal, it’s clear that big data is playing a tremendous role in the world’s response to COVID-19. The use of big data analytics in healthcare is not a novel idea, but today, it’s being used in some interesting — and very effective — ways to slow the spread of this dangerous virus.
Collecting Big Data Analytics in Healthcare During a Pandemic
When a pandemic strikes, epidemiologists and virologists get to work collecting some key pieces of information, such as:
- The number of individuals who’ve received a COVID-19 test;
- COVID-19 test results (positive and negative);
- Exposure incidents and number of people exposed;
- Incubation period and symptom manifestation timeframes;
- The number of sick individuals;
- The number of patients hospitalized with COVID-19;
- The number of individuals who have recovered from the COVID-19 virus; and
- The number of fatalities.
The above-mentioned data is critical for painting a clear picture of the pandemic, its spread and its effect on the population. But this is just the tip of the iceberg. For instance, when analyzing COVID-19 fatality rates, epidemiologists and virologists must consider a host of different factors such as:
- The individual’s age;
- Pre-existing conditions and general health;
- The nature of the transmission incident that led to the individual’s illness;
- The location where they were likely infected;
- The timing (in terms of virus progression) when the individual sought medical treatment;
- The level and type of medical care received; and
- Possible transmission incidents where the virus may have been spread to others.
As you can imagine, these COVID-19 investigations lead to an exorbitant volume of raw data. In fact, this is a prime example of big data and how proper data handling and analysis can have a dramatic impact on a global scale.
How Medical Experts and Scientists Are Using Big Data Analytics to Fight COVID-19
The scientific community, the healthcare sector and government leaders are relying upon accurate data to make informed decisions such as how to control the spread of the coronavirus and what policies or measures ought to be implemented in an attempt to minimize the number of viral infections and deaths. At the end of the day, human behavior is the key determiner for the COVID-19 timeline and the mechanism for how a coronavirus spreads through the population.
In the world of big data analytics in healthcare, predictive modeling is one of the most effective tools in an epidemiologist’s / virologist’s arsenal. Predictive analytics engines can process data in a very efficient manner, spotting trends and patterns that would escape notice by a human analyst.
What’s more, the results of predictive modeling can sometimes defy expectations, making this technology extraordinarily valuable. A wonderful example of this can be observed in a recent article published by The Washington Post. The article includes a coronavirus simulator. The COVID-19 simulations run in real-time, simulating the spread of a fictional virus called “Simulitis.” This fictional virus spreads more easily than the actual COVID-19 virus, so the modeling is more representative of a “worst case scenario.”
The coronavirus simulations run for a few different behaviors:
- No change in social behavior (a.k.a., a “free for all”);
- Moderate social distancing;
- Extensive social distancing; and
- Attempted forced quarantine.
Many might assume that forced quarantine would be the most effective method for flattening the curve for coronavirus infections. After all, if everyone is forced to shelter in place, opportunities for transmission are greatly reduced. But this is a measure that’s only effective in theory; in practice, forced quarantine is woefully ineffective (and not to mention, extraordinarily stressful for the population in question.) It’s simply not possible to get all members of a community to quarantine efficiently — which is why the article refers to it as “attempted quarantine.”
Remarkably, when compared to quarantine, both moderate social distancing and more extreme social distancing are dramatically more effective at “flattening the curve” for this coronavirus proxy.
In the fight against COVID-19, data leads to knowledge and knowledge leads to power. So the role of big data and predictive analytics will only become more essential. Citizens are continuing to change their behaviors and companies are altering their business practices and business strategies in response to COVID-19. As a result, some may require data governance tools such as a data lake or cloud integration, new custom software or mobile app development services. Others may require help establishing a new, secure enterprise software platform to facilitate remote work. This is where the team at 7T can assist, as our team continues to work full-time in a remote capacity amid the coronavirus crisis.
7T is a software development company, dealing in a wide range of custom development projects. Our team of top Dallas software developers specialize in a range of different technologies, including ERP and CRM development, one-of-a-kind mobile app and custom software development projects, tools for data lake creation, data governance, data visualization, cloud integrations and system integrations. In fact, our work speaks volumes!
7T has offices in Dallas, Houston, Chicago, and Austin, but our clientele spans the globe. If you’re in search of your next mobile app or a secure enterprise software solution, contact 7T today.