How AI Can Potentially be Used in the Battle Against Pandemics

In AI by James KobielusLeave a Comment

How AI Can Potentially be Used in the Battle Against Pandemics

The News: The ongoing coronavirus COVID-19 pandemic
has received saturation coverage not only in the mass media, but in
specialized scientific and research channels. In the public interest,
many news outlets have put their COVID-19 coverage outside their

One of the most useful of these free news sources for the technical
community has been the MIT Technology Review. If interested, here is a
link to their free coverage of the coronavirus COVID-19 outbreak. One of the recent articles on this site is a comprehensive discussion
of research into how AI can potentially be used in the battle against
pandemics, and how AI is, artificial artificial intelligence (AI) is
helping to stem the outbreak’s tide. Just as important, the article
highlights the limitations of current AI tools, approaches, and
implementations in dealing with the current pandemic.

How AI Can Potentially be Used in the Battle Against Pandemics

Analyst Take: AI is playing many roles in the
world’s battle against the COVID-19 pandemic. But AI is certainly not a
panacea and its role in helping stem the tide of infections and
mortality should not be overstated.

Pandemics have afflicted the human race for as long as our
species has walked the Earth. As sure as the sun rises every morning and
sets every evening, these devastating viral outbreaks will return and
wreak havoc.

But that doesn’t mean the human race is defenseless in the battle
against contagious disease. Indeed, we have added a powerful new weapon
— AI — in this struggle, and it’s proving its worth in the present
coronavirus COVID-19 pandemic. While there are no doubt many benefits AI
can provide, AI also has its limits, which is what I wanted to discuss
here. With that in mind, let’s go a little deeper.

AI Can Be Used as an Early Warning System

One immediate benefit of AI is that it can be used as an early
warning system. AI enables epidemiologists both to spot emerging
outbreaks and to predict how they might spread from region to region and
perhaps even from one demographic cohort to others.

For example, vendor BlueDot uses an AI-based solution
to monitor outbreaks of infectious diseases around the world. In late
December 2019, more than a week before the World Health Organization
officially flagged the COVID-19 outbreak,
BlueDot alerted governments, hospitals, and businesses to an unusual
spike in pneumonia cases in Wuhan, China. The outbreak was also
identified early by AI-based tools HealthMap (at Boston Children’s Hospital) and Metabiota in San Francisco.

However, AI-automated early warning systems may find themselves
racing against online social channels for the distinction of being first
to detect a new outbreak in the offing. The MIT Technology Review
article that I cited earlier in this article reported that human teams spotted the current Coronavirus/COVID-19 outbreak
on the same day as these AI-powered research tools. That’s not
surprising, considering outbreaks tend to have highly localized initial
stages, in which at least one close-up observer raises an alarm. We are
seeing that play out today, as physicians and healthcare workers the
world overtake to social media channels, private or otherwise, to share
concerns, thoughts, observations, and we are also seeing that as
citizens of affected areas share their stories. Social media channels
are powerful conduits of information

Bottom line, AI can be used as an early warning system, but let’s
not overlook, or underestimate in any way, the power of human to human

The Potential for AI as Infection-Path Predictor

I think there is great potential for AI being used as an
infection-path predictor, predicting how COVID-19 or any other outbreak
is likely to spread, and, just as importantly, how tactics such as “social distancing” might curtail or even lessen its severity.

In theory, it might be possible to run unsupervised learning
algorithms that simulate all possible evolution paths, experiment
digitally with how well potential vaccines perform in each scenario, and
even determine whether and how the viruses develop resistance through
mutations. But this approach is a bit far-fetched to offer near-term
hope in the current pandemic. That’s due to the need for rapid advances
in the science, modeling, and computing capabilities that would be
needed to pull it off.

Another practical obstacle is the need to find sufficient amounts
of behavioral, social, clinical, airline, and other data sources of
sufficient quality to build and train accurate enough machine-learning
models of an outbreak’s likely evolution path. The companies that
detected the current COVID-19 outbreak were using NLP algorithms to look
for relevant reports coming from news outlets and official health care
channels in different languages around the world. However, especially in
fast moving viral outbreaks, those sources may be too vague,
inconsistent, and biased by political, cultural, and other factors to
offer the proverbial “single version of the truth.”

In addition, the chances of pooling this data from diverse global
sources in the middle of a fast-moving pandemic are not great, and the
difficulties of harmonizing and cleansing it all are so great that the
effort would take longer than the pandemic itself to come to fruition.

It’s also next to impossible to find reliable data on “social
distancing” variables of a behavioral nature, such as the incidence of
handshaking, the frequency with which people wear surgical masks and
gloves in public, the average size of public gatherings, and so on. As
one of the researchers in the MIT article states: “We…don’t really know
what behaviors people are adopting—who is working from home, who is
self-quarantining, who is or isn’t washing hands—or what effect it might
be having. If you want to predict what’s going to happen next, you need
an accurate picture of what’s happening right now.”

Where behavioral factors come in, there’s the need for high
degree of predictive precision to drive proactive alerting of the
relevant countries, regions, and authorities. The efficacy of such
tactics as quarantines, school closures, and vaccination of at-risk
demographics depends on having early enough intelligence so that
outbreaks can be squelched before they spin out of control.

But having early warning is not enough in a fast-moving public
emergency. All the AI-driven insights in the world are powerless in a
situation such as what we’re facing in the United States and a federal
government that has been slow to respond, less than transparent, and
difficult to trust to have the best interests of the public first and

No matter how powerful its tools and accurate its data, AI can’t
immunize us against a political establishment that refuses to take
effective timely action.

Using AI as Diagnostic Instrument

AI is being used to examine medical images for early signs of
many diseases that human doctors might miss. In recent weeks, preprint
research papers have begun to appear online in which machine learning has been shown to diagnose COVID-19 from CT scans of lung tissue.

However, this approach might not be effective as an early
diagnostic, considering that physical signs of the disease may show up
in scans only after infection, making it not very useful as an early
diagnostic. Also, the paucity of training data on a disease so new makes
it difficult to assess the predictive accuracy of the approaches in the
research literature, especially where it concerns identify subtle
patterns in medical images.

Techniques such as few-shot learning and transfer learning
might be used to train AI models to look for COVID-19 in the absence of
much training data, but those approaches remain largely unproven for
the current outbreak.

Exploring AI as a Research Discovery Tool

AI can accelerate access to a vast, constantly changing corpus of
research literature, data, and analytical tools pertaining to
outbreaks, their spread, and effective treatments.

This recent MIT Technology Review article discusses a new open database—known as CORD-19
(COVID-19 Open Research Dataset)—which contains over 29,000 coronavirus
research papers. Researchers from several organizations released the
Covid-19 Open Research Dataset, which includes papers from peer-reviewed
journals as well as preprints from websites such as bioRxiv and
medRxiv. The research covers SARS-CoV-2 (the scientific name for the
coronavirus), Covid-19 (the scientific name for the disease), and the
coronavirus group. It was compiled under the request of the White House
Office of Science and Technology Policy (OSTP).

The database, now available on AI2’s Semantic Scholar website, leverages AI to speed searches through academic literature. It incorporates natural-language processing models such as ELMo and BERT to map out the similarities between papers and create personalized feeds based on researchers’ interests. The OSTP also launched an open call
for AI researchers to develop new techniques for text and data mining
that will help the medical community comb through the mass of
information faster.

Though no one can dispute the value of this database or the need
for more powerful AI tools to search it, it’s clear that most insights
that researchers might gain from them over the next 4-8 weeks will apply
more to the next pandemic—which could be decades in the future—but
probably won’t come in time to be much use in combatting the current
outbreak. And most research studies included in the database now are
probably derived from studying previous outbreaks, limiting their
usefulness in devising strategies for dealing with today’s unfolding

AI as Treatment Tool

AI can facilitate medical researchers’ investigations into pharmaceutical and other treatments to arrest COVID-19’s progress and possibly find a cure.

Though there’s no proven treatment yet, the World Health Organization has identified
more than 70 drugs or “therapeutic” combinations thereof that are
potentially worth trying. It’s highly likely that AI-based tools such as
this experimental DeepMind offering
are being used to explore how protein structures and interactions how
the virus functions and how, conceivably, it can be neutralized. In
addition, generative design algorithms
use AI to produce millions of candidate biological or molecular
structures and sift through them to highlight those that are worth
looking at more closely for their possible efficacy.

However, this approach may be too little too late, because it can
take months before a promising candidate emerges from the pack.

The Takeaway on the Potential of AI in Battling Pandemics

Clearly, AI is stepping up to the many challenges of dealing with
current coronavirus COVID-19 outbreak. It has proven to be an
invaluable tool for detecting the pandemic’s onset; predicting when,
where, how, and at what speed it’s likely to spread and evolve;
diagnosis its incidence and severity; and discovering effective cures
and treatments.

However, AI is largely standing on the sidelines when it comes to
helping people, groups, businesses, and government agencies to cope
with the outbreak. Though simulation tools like this,
developed by the Washington Post allude to the possibility of
“flattening the curve” of the pandemic’s spread through “social
distancing,” the underlying technology doesn’t seem amenable to
packaging into a personal digital assistant of the sort that would be
needed to help each of us avoid exposing ourselves to the virus in the
normal course of living our lives.

Even if it were possible to have our own personal recommenders
that steer us away from behaviors that might expose us to coronavirus
COVID-19, many people would find these tools so intrusive and nagging as
to be practically unusable.

Futurum Research provides industry research and analysis.
These columns are for educational purposes only and should not be
considered in any way investment advice.

Related content from our Futurum Research Team:

3 Simple Digital Transformation Principles That Will Help Your Business Adapt to the Coronavirus Crisis 

Zoom Stock Finds a Bright Spot in Coronavirus Fears 

Zoho Announces ESAP: Seeks To Help Small Business With Free Software

Image Credit: US and News Report


The original version of this article was first published on Futurum Research.