The effects of the Coronavirus (or COVID-19, as it has been officially named) outbreak have been felt all over the world, including the tech community, but according to at least one expert, the technology tracking the outbreak has improved dramatically over the years.
SAS’s director of government practice, Steve Bennett, said a key to tracking and containing such disease outbreaks actually lies in data analytics.
The major benefit of using such solutions, said Bennett, lies simply in the added time they save health organizations.
“Analytics tools are becoming indispensable to understand and be able to rapidly respond to outbreaks,” said Bennett in an interview with IT World Canada. “Speed is everything. Speed saves lives. So the ability to understand quickly when you have a new outbreak or new disease… the quicker you can do that, the more quickly everything else can get started to help mitigate it. Analytics provides a previously impossibly fast way to get insights out of information.”
Bennett pointed to three types of analytical solutions that he said can be crucial: data management and sharing platforms, data visualizer tools, and AI and machine learning tools.
Advancements in analytics
As of this article being published, the World Health Organization reported 79,331 confirmed cases worldwide for COVID-19, with 2,618 confirmed deaths.
Almost 20 years ago now, SARS infected 8,090 people, of which 774 died, according to stats from the CDC.
Bennett said that he has seen two major advancements in the analytics field since then.
The first of those advancements being the development of AI and machine learning, which Bennett said has had great effects in speeding up the development of vaccines when applied to data about past diseases to find genetic similarities.
What in the past took up to 10 years, now takes only about 18 to 24 months. And in fact, just last year, scientists in Australia unveiled the first AI-developed vaccine in the world.
“It really speeds a lot of things up. We can make better sense of much larger pools of data and get better insights from them much faster than we could previously,” he said. “But it’s still not good enough. We want to use this technology to make things go even faster.”
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The second major advancement that Bennett noted was not a technological advancement at all but was instead the popularization of the practise of sharing data.
“One of the biggest things that we’ve seen since 2003 is more openness among other countries and health organizations in sharing data. If you don’t have the data… or it’s old or it’s incomplete, you’re really starting blind,” he explained. “We’re seeing better openness about sharing information. The quicker you can get information shared globally, the more quickly the best minds and the best analysts can start to work on it. And it really does save lives.”
Looking to the past
As analytical tools rely on data, past events can be key in similar events in the future. Even with a variance in behaviours among the countless number of existing and past diseases, Bennett said there is still great value in leveraging the data.
“There’s a lot you can learn from the past. There’s always something that’s going to be a little different with it with the new virus, but there’s so much we can learn from past pandemics,” explained Bennett. “Pulling the data together is the key to really unlocking what you can learn from those previous epidemics or outbreaks.”
As an example, Bennett said scientists can use past data to compare the genetic makeup of the new disease to past diseases and use any similarities to map out a strategy, as it can indicate how those diseases passed themselves from animals to humans, and then subsequently, how they spread among humans.
From insights to action
But all of this is for naught, said Bennett, if the insights gained from these analytic tools are not turned into actions on the ground, not only in preventing current pandemics but also in preparing for future ones.
“We can do all the data analysis we want, but if it doesn’t really help somebody take better public health or policy action, none of it matters. It’s just a science project,” explained Bennett. “What are the right policy changes and the public health policy decisions that have to be made as a response to what we saw in the outbreak so that we can limit the impact of the next one? And this is an area where we think advanced analytics has a real benefit. Things like policy simulation where you can use analytics to try out different what-if scenarios. That’s what it isn’t talked about as much when you’re in the midst of an ongoing crisis. But I think it’s really important these same tools be used when there isn’t a big crisis, to help you test out different policies and approaches to see which might be most effective. That way, you’re smarter and faster when that next one comes around.”
