The new data may also change some expectations about the need for proximate work. In some cases, remote work may become possible and desirable. In manufacturing, the collaboration of intelligent collaborative robots and humans could also mitigate risk. The massive effort to use computation to fight the virus presents the opportunity to fundamentally redesign the way essential services are delivered and preserve the functions of society in crises. It’s time to deploy those computing resources.

Such risk analysis can work much the way storm warnings do. However, starting and stopping business and social activity based on forecasts will be a new challenge that needs to be addressed. Analytics that are typically applied to forecasting demand, costs, and other factors can be redeployed to help mitigate this challenge. Analytics and computational power can also be applied to helping human resources more effectively weigh the risks of physical collaboration.

Their call for a plan begins with the rapid deployment of testing infrastructure and supplies for frontline workers. This buys the time to start putting technology solutions in place. Those technology solutions include smart, privacy-respecting contact analysis through data in positive patients. Analysis of this data helps determine hot spots and, even more crucially, cool spots where risk analysis suggests that people can start convening again.

Right now during the global pandemic, the awful choice seems to be between saving lives or saving livelihoods. The authors — one a roboticist and AI researcher, one a public health researcher and physician — believe it’s imperative to create a massive investment in data, analytics, and computing power to combat the virus.

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One of us (Julie) is an AI researcher and a roboticist at MIT. The other (Neel) is a physician at a major hospital and a public health researcher at Harvard. Our dinnertime conversations tend to focus on the future. Lately, unsurprisingly, they’ve become hushed and grim.

Amid the daily news churn, policy makers seem to be facing an impossible choice between saving lives and saving livelihoods. A close study of cautionary tales and hopeful examples from across the globe makes clear that social distancing, sheltering in place, and other mitigation efforts are critical to blunting the impact of the pandemic, despite the havoc they wreak on daily routines and markets. However, we know that the sooner we can return to safely congregating, the better.

How can we get there? We believe the answer lies in computation. We need to put as much data and computing power into the problem as we can, and now. Here’s a hopeful scenario we’ve discussed, one we believe could, with focused effort, be operational by summer.

The first step is getting the basics in order. South Korea is producing 100,000 test kits per day, and it has conducted more than 300,000 tests to date. That amounts to more than 40 times the per capita rate of testing in the United States. We need rapid and available testing capabilities. In fact, we needed them some weeks ago; now we must do everything we can to ramp them up as quickly as possible. We also need adequate supplies of personal protective equipment for health care workers and others on the front lines, along with ventilators and other lifesaving treatments. Putting those things in place, in combination with the mitigation measures currently being deployed, including lock downs of entire states, will buy us critical make-up time for deploying data and computers against the virus.

The next step is developing smart prevention capabilities rather than requiring blanket isolation and shutdowns. Our window is short — measured in months — for heading off what Bill Gates has characterized as potentially a once-in-a-century pandemic like the 1918 Spanish Flu, which killed at least 50 million people around the world. We have many technological advantages over those fighting that pandemic a century ago. In many ways, this is our most meaningful Big Data and analytics challenge so far. With will and innovation, we could rapidly forecast the spread of the virus not only at a population level but also, and necessarily, at a hyper-local, neighborhood level.

At MIT, efforts are underway to use existing mobile technologies to quickly develop game-changing, privacy-preserving contact tracing. When someone tests positive for COVID-19, health care providers could download the names of those who were in close proximity to the infected individual during the relevant time frame without accessing their comings and goings. With that anchoring information, computer scientists could then integrate data from a broad swath of sources — possibly including the amount of virus in wastewater — to forecast precise community-level infection risks.

That data would allow more-dynamic risk assessments, sufficiently precise and current to allow us to decide not whether schools and workplaces should be open but which ones should be open, and for how long. Air traffic controllers harness computing to coordinate the use of airspace in the face of uncertain weather patterns. A high viral-risk day for a specific locale could be the epidemic equivalent of a storm warning.

Such targeted isolation strategies would allow many more schools and businesses to stay open, which would be good. It would create challenges, too. Starting and stopping operations on the basis of current risk is not trivial; it can wreak havoc on supply chains and daily routines. Computing technology could make the process less disruptive and help ensure that we meet procurement needs and preserve workforce continuity. Logistics and transportation companies such as FedEx use human-artificial intelligence collaborations to plan their supply chains according to factors including predicted demand and transportation costs; similar collaborations could be deployed to improve the flexibility of our workplaces and schools.

Adapting such measures to fight the pandemic might teach us that physical presence is not always as necessary as we had thought. Remote work may simply become part of how we think about work. Here, too, computing could allow us to finely weigh the risks and benefits of having people work alongside one another. Much as sports franchises have used advanced analytics to compose their rosters, businesses and other organizations could develop metrics to team people up in risk-informed ways. New human-robot interface technologies, which allow users to communicate with robots that are stocking supplies, cleaning, or assembling equipment, effectively allow people to collaborate with machines as if working with human partners. The increasing availability of smart virtual-collaboration tools and intelligent collaborative robots in industrial spaces will be game-changers for remote work.

Amid an unprecedented pandemic and facing an uncertain future, we’re all adjusting to rapid and drastic changes to our daily lives. As the tumbling markets indicate, the pace of change is straining the well-oiled operations and infrastructure that hold society together. Hard though it may be, we innovators must wrench ourselves away from the operational details of managing these hardships and look unflinchingly forward. We need to not only soften the blow of curtailed timelines and busted budgets but fundamentally redesign the way essential services are delivered and preserve the functions of society. We have the people. We have the data. We have the computational force. We need to deploy them now.