Marketing and user experience studies are among those that have been conducted via big data analysis. More recently, a variety of companies in the insurance sector have relied on this information to help them make data-driven decisions.
However, it now looks like computer scientists are applying this technique toward the fight on coronavirus. Media pundits have focused on the fact that tech startups are now connected with clinicians, academics and other more conventional research entities in the hopes of leveraging every outlet possible in order to fight infection.
Analytics, however, are going far beyond merely providing actuarial statistics in this case, however.
How Analytic Information can Stop Infection Trends
Some larger data centers are using information collected from otherwise standard analytic computer software packages to trace the paths of people who might now be infected but weren’t known to be at the time. That can help to reduce the risk of running out of precious healthcare resources when new infections start to spring up.
At the same time, these packages can help to predict certain types of shopping trends, which is becoming increasingly important to know as a result of the goods shortages that are ravaging the world’s normal supply chains. When an algorithm predicts that an area might receive a sudden uptick in the number of people consuming a large amount of some product like toilet paper or respirator masks, it can help to ensure that at least some amount of these goods get to the location before it becomes a major issue.
Perhaps the most impressive aspect of this technology, however, has nothing to do with these simple economic calculations. Rather, it’s the way that big data gear is helping to unfold proteins and potentially discover new antiviral drugs.
Distributed Computing Helps Smaller Research Labs
Distributed computing projects that focus on modeling molecular dynamics might actually have the biggest role to play in any big data-centered attack on coronavirus infections. Years ago, top bioinformatics experts like Vijay Pande managed the so-called Folding@Home protocol. This system continues to provide information about viral DNA and RNA strands by running computer simulations on a wide variety of systems around the world. Individual end-users install a client program in order to make this possible.
It’s likely that various other programs will follow the Taiwanese model when it comes to collecting data and continue to use systems like this to process information on an unprecedented scale. Scientists in Taiwan have been relying on analytics information to develop a better response to the virus than they have thus far.
As a result, they’re quickly learning more about the peplomer spikes that coat the coronavirus itself. These are made from glycoprotein molecules that jut out from a viral capsid and then bind to receptors on the host cell. This is precisely what permits the coronavirus to attach itself to human cells and begin injecting its own DNA in favor of a new matrix.
Over time, more information about how these spikes work could help medical engineers to design new antiviral drugs to inhibit the viral lifecycle from taking place. After a period of time, a single instance of coronavirus will have to start looking for new host cells to infect once it completely exhausts the existing infected cell of all resources. By looking at this data, it might be possible to discover some sort of method of preventing this from happening. More than likely, there’s some manner in which healthcare technicians could somehow tamper with its protein binding mechanism.
Perhaps more interestingly, though, a review of the data is quickly showing a great deal of insight related to nutrition.
Nutrition & Demographic Data Helps Fight Coronavirus
Scientists are reviewing existing data models to try and predict just how many people will contract some form of coronavirus infection. At the same time, they’ve found that traditional means of boosting immunity might have some role to play in doing so.
This mirrors previous research on disease prevention, which involved data analysts using existing information and leveraging it in new ways. While this previous research focused primarily on Alzheimer’s disease, there’s no reason that the same paradigm can’t be applied to many different issues.
In fact, it’s easy to assume that data scientists might someday apply it to preemptive research programs that are designed to calculate the risk of diseases and immunodeficiencies on a global scale before they ever become a major issue. This would give public health officials plenty of time to prepare themselves and put certain restrictions in place before anyone would actually have to suffer.
At the same time, the current chaos caused by healthcare-related issues have spurred on the growth of various scams. That’s meant that big data analysis firms have also been dealing with a number of problems related to information theft.
Once again, they’ve stepped up to the plate and applied this same technology to the data security field. This can help to reduce the risk of opportunistic attacks while also continuing to buy time for those who are busy doing their best to develop new treatments for the outbreak.
