Data Science Can Slow Down the Spread of COVID-19
We know that data and analytics play a role in everyday
products - from music recommendations we'd like to hear to automated re-routing
through our GPS system. But how can the power of analysis be harnessed in a
disease that currently threatens the health and economic well-being of people
around the world?
If we go back in time to the 1850s, there are two
significant examples of how the early pioneers of data science had incredible
impacts on the world that can provide a glimpse of what we might see happen.
then.
With the rapid spread of the new Coronavirus around the
world, researchers and scientists are rushing back in time to find
breakthroughs that can help us contain the global epidemic. Although new
information arrives, we still have to find many ideas to fight against this
widespread disease.
Analysis is also an effective way to combat the
proliferation of misinformation and inaccurate data, which is perhaps even more
dangerous than the pandemic. Even after the global pandemic ends, data analysis
can be useful in helping us cope with social, economic and political
consequences.
Whether you have a background in analytics or are
considering pursuing a career in this field, it may be helpful to know more
about how your skills can be put to use by your business in these unprecedented
times. Read on to learn more about the role of data analytics in these
difficult times.
A powerful case of data and analysis exploited to bring about
a significant change in the course of the spread of a disease.
It was in 1852, and the cholera pandemic had spread to
London. More than 23,000 people have already died. To make matters worse,
unbalanced press reports have led people to believe that victims were more
likely to die in hospital than at home and that doctors would kill their
patients for anatomical dissection, a result known as "Burking".
John Snow, who is often described as the father of
epidemiology began to geospatially analyze the deaths in London and isolate
the source of the disease, a contaminated well that supplied water to the
London Soho area.
Using his analysis, he convinced local authorities to remove
the handle from the pump and cholera cases quickly dropped, halting the spread
of the disease in London.
A few years later, in roughly the same geography, a young
nurse, Florence Nightingale, resolved another important medical problem. The
British Empire was at war with the Russian Empire and thousands of soldiers
were hospitalized. Conditions in hospitals were horrible and the chances of
survival once admitted were less than 60%.
Nightingale was data-driven and, when implementing new
procedures (such as hand washing), methodically recorded data on how each
performed and analyzed the results. One of the most famous reports showed how
his practices in these field hospitals reduced death rates from 42% to 2%. And
if that was not enough, Nightingale collected data on these same rates from the
best hospitals in London to show that these innovative practices should be
implemented everywhere.
Many of these methods used to reduce the spread of disease
are still practiced today. Believe it or not, during this time, most believed
that bad odors were the cause of the spread of disease.
These first two pioneers of data science paved the way for
many others. In both cases, they were field experts - trained in medicine. They
had access to the data and understood how to analyze the data to generate
results. And this model continues to repeat itself in more modern examples.
In another type of epidemic, during the 2009 avian flu
pandemic, we saw Alteryx taken advantage of by the USDA to respond with
incredible speed to stop the epidemic. Using geospatial data and the modern
Alteryx analytics platform, the agency was able to conduct field analyzes
faster than before, helping to end the epidemic quickly and reduce the economic
impact.
Identifying the Important Trends and Correlations in Data
Since there is no treatment or treatment for coronavirus at
the moment, preventive measures are the best line of defense. Data analysis has
been particularly useful in identifying correlations and patterns between
different factors and determinants from the massive amounts of patient data that
can determine risk factors.
The analysis was also useful in recommending quarantine and
disease protection strategies.
Analyzing the Global Information Systems data
Global information systems have proven to be an important
tool in the search for data to determine the spread of the disease in different
countries. Data analysis tools such as data mining and predictive analytics can
help analyze data from global information systems to facilitate the discovery
of treatment models and clinical outcomes from drug trials. Evaluating data
from global information systems can also help experts identify the causal
factors behind the epidemic.
Data Science Continue to Be Leveraged to Stop the Spread of the Coronavirus?
When I got off a plane recently, I was interviewed by the
CDC on the basis of analyzes which showed that I had traveled in a high risk
area. It is certainly an excellent analytical use case and incredibly easy to
implement on modern analytical platforms. But I think there are even more
breakthroughs to come with even greater effects, whether in vaccine analysis or
containment methodologies, in treatment effectiveness analysis or in new
procedures to protect first responders.
I expect extraordinary people with great experience and
knowledge to continue to take advantage of advanced analytical tools and
techniques to change the world, and I expect to hear more examples of the how
COVID-19 fits its superhero today - the knowledge worker with data science
skills.
Predicting Protein Structures to Facilitate Vaccine Development
Analytical approaches such as deep learning and machine
learning can also be extended to study and predict the shape of protein
molecules that contribute to the virulence of the coronavirus.
Machine learning can help recreate molecular interactions at
the cellular level, which can bring us closer to developing vaccines for
COVID-19. Machine learning and analysis can also determine the effectiveness of
current antiviral drugs and vaccines in reducing the effects of the pandemic.
- Analytics and Big Data are essential to understanding and fighting the spread of deadly diseases.
- Domain knowledge and access to data - as well as an understanding of how to analyze data - are key factors for positive results.
- Data science and Alteryx can help you change the world.
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