Having participated and contributed to many programs on big data, artificial intelligence and health, I realize that yes, medicines and vaccines are needed to treat the COVID-19 virus, but big data analytics can also be mustered into service to prevent the spread of the insidious pandemic that now grips the world.

This isn’t just a hypothesis by an analytics professor who has spent two decades in the analytics and machine learning area, but it is actually being proven in Taiwan, an island nation that is in close proximity to mainland China, where the virus was first reported in the city of Wuhan.

As of the week of March 16, Taiwan reported about 100 detected cases of COVID-19.

Yes. 100. Not 1,000. Or 10,000. Taiwan is about as close to the epicenter of this outbreak as any place can get. There is a lot of travel from the Wuhan area to places in Taiwan. Despite this, just 100 cases? And it’s not because they are not testing people. By comparison, the United States reported 16,638 cases and 216 deaths.

How does Taiwan keep their exposure so low?

Learning from prior epidemics, Taiwan has patiently built a country-wide, big data-driven methodology to tackle a very specific problem, according to an article in this month’s issue of JAMA. In short, Taiwan has perfected the science of using analytics for managing outbreaks before they affect their population.

The good thing about people who travel a lot is that they bring a lot back. The bad thing about people who travel a lot is that they bring a lot back. We need their ideas, not their pathogens. By integrating travel data with data in their health systems, Taiwan is able to tell what a traveler needs to be tested for based on where they've been.

People testing positive can be tracked through their phones to ensure that quarantine is maintained, and perhaps use that data to see who else might be vulnerable and needs to be tested. There was also a “big tent” approach that included efforts in fighting disinformation and involving the right agencies for providing accurate information.

Can we do this in the United States? Maybe.

There will be some privacy sacrifice (data sharing across travel and health databases for instance). Perhaps we can design privacy-sensitive ways for doing this and blockchain-based ideas might work here. It will probably require annual funding that is significant (likely in the billions of dollars) to do this well. But chances are it can generate economic activity proportionally, both by virtue of these activities, as well as keeping people healthier so they can work.

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What is the cost of not doing this? Ask Wall Street.

In addition to loss of lives, jobs, fear, and massive disruption, the COVID-19 crisis resulted in a loss of $6 trillion in wealth in just six days in the United States from the drop in markets alone. While some of this will come back as the market recovers, nobody wants to go through this again.

What next?

Our leaders have told us what they are doing economically, what they are doing to make tests available, and what they are doing to contain this spread – including seemingly draconian shutdowns to “flatten the curve” – which is absolutely the right thing to do at this point. Schools are being shut down across the country for weeks. Besides the loss time in their education, some 30 million young students will not get daily meals. These students will likely eat much less given the loss of wages to their parents in this crisis.

As politicians like to say, never let a crisis go to waste. Our leaders should now present a framework for the future, possibly similar to what was done in Taiwan, to help prevent these types of situations in the future. The markets and the citizens will react much better if they know a plan for the future exists, where they can have confidence that things like this will happen only once, since once is enough. We need a “Niche Problem Big Data Big Tent” approach now that leverages analytics, technology and brings together private and public entities in a “big tent” collaborative framework. Much like the one we just saw in Taiwan, a country which could have had 100,000 COVID-19 cases, but had only 100. Anyone think there isn’t an economic case for doing this anymore?

Balaji Padmanabhan is a professor in the University of South Florida Muma College of Business’ Information Systems and Decision Sciences Department and director of the college’s Center for Analytics and Creativity.