Our ability to collect data far outpaces our ability to fully utilize it—yet those data may hold the key to solving some of the biggest global challenges facing us today. Take, for instance, the frequent outbreaks of waterborne illnesses as a consequence of war or natural disasters. The most recent example can be found in Yemen, where roughly 10,000 new suspected cases of cholera are reported each week—and history is riddled with similar stories. What if we could better understand the environmental factors that contributed to the disease, predict which communities are at higher risk, and put in place protective measures to stem the spread? Answers to these questions and others like them could potentially help us avert catastrophe.
We already collect data related to virtually everything,
from birth and death rates to crop yields and traffic flows.
IBM estimates that each day, 2.5 quintillion bytes of data
are generated. To put that in perspective: that’s the equivalent
of all the data in the Library of Congress being produced
more than 166,000 times per 24-hour period. Yet we don’t really
harness the power of all this information. It’s time that changed—and
thanks to recent advances in data analytics and computational services,
we finally have the tools to do it.
For example, knowing mosquito incidence in communities would help us
predict the risk of mosquito-transmitted disease such as dengue,
the leading cause of illness and death in the tropics. However,
mosquito data at a global (and even national) scale are not available.
To address this gap, we’re using other sources such as
satellite imagery, climate data and demographic information to
estimate dengue risk. Specifically, we had success predicting
the spread of dengue in Brazil at the regional, state and municipality
level using these data streams as well as clinical surveillance data
and Google search queries that used terms related to the disease.
While our predictions aren’t perfect, they show promise. Our goal
is to combine information from each data stream to further refine
our models and improve their predictive power.
Similarly, to forecast the flu season, we have found that Wikipedia
and Google searches can complement clinical data. Because the rate of
people searching the internet for flu symptoms often increases
during their onset, we can predict a spike in cases where clinical data lags.
We’re using these same concepts to expand our research beyond
disease prediction to better understand public sentiment.
In partnership with the University of California, we’re conducting
a three-year study using disparate data streams to understand
whether opinions expressed on social media map to opinions
expressed in surveys.
For example, in Colombia, we are conducting a study to see
whether social media posts about the peace process between the government
and FARC, the socialist guerilla movement, can be ground-truthed
with survey data. A University of California, Berkeley researcher
is conducting on-the-ground surveys throughout Colombia—including
in isolated rural areas—to poll citizens about the peace process.
Meanwhile, at Los Alamos, we’re analyzing social media data and news
sources from the same areas to determine if they align with the survey data.
If we can demonstrate that social media accurately captures
a population’s sentiment, it could be a more affordable,
accessible and timely alternative to what are otherwise expensive
and logistically challenging surveys. In the case of disease forecasting,
if social media posts did indeed serve as a predictive tool
for outbreaks, those data could be used in educational campaigns to
inform citizens of the risk of an outbreak (due to vaccine exemptions,
for example) and ultimately reduce that risk by promoting protective
behaviors (such as washing hands, wearing masks, remaining indoors, etc.).
All of this illustrates the potential for big data to solve
big problems. Los Alamos and other national laboratories
that are home to some of the world’s largest supercomputers
have the computational power augmented by machine learning and
data analysis to take this information and shape it into a story
that tells us not only about one state or even nation,
but the world as a whole. The information is there;
now it’s time to use it.
How Big Data Can Help Save the World, https://blogs.scientificamerican.com/
By Sara Del Valle on March 27, 2019
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System