The US isn’t using data that could save people from getting coronavirus

The data we need to curb the coronavirus pandemic is there. Public health systems need to use it.
The data we need to curb the coronavirus pandemic is there. Public health systems need to use it.
Image: AP Photo/Seth Wenig
We may earn a commission from links on this page.

While we may not have seen the novel coronavirus and Covid-19 respiratory disease before, we have certainly seen the damage infectious disease threats pose. When I led the US response in West Africa to the Ebola outbreak, I saw the cost of disease firsthand. I also saw how leveraging technology and real-time data—during the Ebola crisis this was information from cellphones— enabled public health authorities to help direct limited resources to people and health systems in the greatest need, and ultimately stem the spread of that deadly disease.

First reported in China, Covid-19 has so far been detected in more than 160 countries at the time of publishing this article. While initially criticized for being slow to act and opaque in disclosing the numbers and locations of cases, China’s system of universal healthcare coverage has proven to be critical in addressing the outbreak. By investing in public health systems, it seems to have at least slowed the curve of new cases for the time being.

Starting behind

In the US, along with many less developed economies, it’s been a different story.

Susceptibility to infectious disease outbreaks will be a hallmark of the 21st century, spurred by a culmination of factors: Air travel continues to set records, while growing urbanization pushes more people together. Meanwhile, extreme weather events from large-scale fires to mass floods are changing disease patterns and straining health systems worldwide, particularly those which still lack protections against even basic health risks.

The global response is not moving with the speed, intensity, and cooperation that this disease demands. This was reiterated by Dr. Toni Fauci, director of the National Institute of Allergy and Infectious Disease, who said in a recent White House press conference, “When you’re dealing with an emerging infectious diseases outbreak, you are always behind where you think you are.”

Open, real-time data, shared across international borders, is critical for leaders to make the right decisions at the right time to slow the virus’s spread. We saw this when Singapore and Hong Kong used public health information to respond their first cases of Covid-19 disease, setting their countries on a track to contain the disease. Unfortunately, these data are just not yet readily available everywhere.

Data wins

The idea that data insights should underpin public health decision-making is more than 100 years old. Data is what inspired the Rockefeller Foundation to create the China Medical Board in 1914, after seeing an opportunity to modernize medical education and improve the practice of medicine in China. This was one of the earliest models for science-based public health and it’s still active, including in the coronavirus response. What is new today is that big data and machine learning can now help public health take dramatic leaps forward—enabling us to predict threats before they happen, rather than waiting until it’s far too late to respond.

We now know that a Canadian technology startup detected an early warning of coronavirus. By tracking fluctuations in commercial flight reservations and social media conversation, the firm pieced together a picture of people in distress weeks ahead of other reports.

This is all to say that the data is there. We just need to make sure public health systems are prepared to have it and use it—especially during an outbreak.

This approach already exists in certain sectors of our public health system. The work of those Canadian researchers, for example, built upon what US health departments are also learning about predicting and stopping foodborne illness in restaurants by monitoring multiple data sources—past health code violations, Yelp reviews, nearby construction permits—and, crucially, acting on those insights.

Despite the promise of data-driven insights to transform public health, with potential applications as diverse as opioid addiction, hypertension, and malaria, actual instances of this happening successfully remain the exception rather than the rule. Too often, insights are never generated, or if they are, official systems are unprepared to respond and act in real time.

Spending money to upgrade outdated public health systems should be viewed as an investment, not a cost. The real cost is inaction; total financial losses of a moderately-severe to severe pandemic have been estimated at more than $500 billion annually. And that impacts all of us: Since the SARS outbreak, to which the current coronavirus is often compared, China’s share of the international economy has tripled, underscoring the fact that managing infectious diseases requires a deeply transparent global response to avoid lasting consequences for worldwide economic growth.

Make what’s private public

To predict and prevent future outbreaks, government health decision makers should be properly trained and empowered to act on predictive data. Many of the datasets and technologies that will help public health systems save lives reside in the private sector. It is crucial to establish shared goals, operational protocols and, most importantly, trust. A good starting point is agreeing on pathways for sharing de-identifiable data, such as aggregate search queries and cell tower pings, which could provide essential public health guidance while protecting individual privacy.

In the coronavirus emergency so far, the private sector is taking steps to protect their employees and customers. Pharmaceutical companies have pledged to develop new vaccines; Chinese telecom companies have sent billions of text messages with public health updates; and Silicon Valley firms are coordinating with international health authorities.

But the public sector remains far behind. More effort needs to be focused on building a lasting system to identify, share, and utilize real-time data through a coordinated, unified model of public-private partnership.