The COVID19 pandemic, like many others of the past century, is often compared with the 1918 Spanish Flu pandemic. Both diseases ravaged global populations thanks to the novel nature of their viruses of origin, and both resulted in fears sometimes verging on hysteria.
The responses to the two pandemics couldn’t be more different, however. When large numbers of people became very ill in 1918, no one at the time knew what was responsible. Theories ranging from a planetary misalignment to tainted oats abounded. After all, viruses were not identified until 1933.
In 1918, antibiotics hadn’t been discovered. While the life-saving drugs don’t fight viruses, they are useful in treating bacterial infections secondary to the virus. One treatment commonly prescribed in 1918 was a high dose of aspirin, which is now known to actually worsen symptoms associated with pneumonia.
Fast-forward to 2020, and scientists are tracking the novel coronavirus in a way never before possible.
In 1918, people were afraid. People were dying en masse, but they didn’t know the cause. They didn’t know how to control spread of the disease, nor did they know of any effective treatments. People in 2020 now have answers to many of those questions relating to COVID19, yet they are still afraid. Could it be that we now know too much?
Thanks to big data, we can now track the virus, which helps scientists design ways to fight the disease. But that same tracking can create forecast models – like this one created with the SAP Analytics Cloud – that portray very precarious futures. Instead of fear created by an information vacuum, it now is the product of an infodemic, thanks in large part to big data.
The battle between the novel coronavirus and big data is at least twofold. The first step requires understanding where the outbreaks occur and forecasting where to expect them next. By combining big data with AI, experts can more accurately create forecast models and compare them to each other based on practically any variables.
Beyond forecasting its path, the next step is developing better prevention tools, which is also aided by data analyzation. Scientists from MIT, for example, are developing contract tracing tools that not only identify anyone who might have come into close proximity with a COVID19 patient, but also do so while protecting the privacy of all involved.
No longer would it be necessary to close communities en masse when potential cases could be individually identified. Data even can help identify what communities are failing to follow social distancing guidelines. Scientists are collecting location data from millions of mobile devices to make similar determinations.
“The near real-time COVID-19 trackers that continuously pull data from sources around the world are helping healthcare workers, scientists, epidemiologists and policymakers aggregate and synthesize incident data on a global basis,” Parexel Chief Medical and Scientific Officer Sy Pretorius told Forbes. “There has been some interesting data resulting from GPS analyses of population movement by region, city, etc., which ultimately helps provide a view of the population’s compliance — or lack of compliance — with social-distancing mandates.”
Knowledge is almost always power, but there’s only so much the human brain – even that belonging to the smartest scientists – can compute.
