COVID-19 has brought the arcane work of mathematical disease modelers to the forefront, as politicians search for ways to flatten the curve.

Why it matters: Models are the only way we can plan out effective steps now to prevent more deaths in the future. But modeling a disease in mid-pandemic isn't easy, and important nuance can be lost in the translation between academic modelers and policymakers.

Driving the news: During the White House briefing on Tuesday, Anthony Fauci and Deborah Birx displayed graphs showing government projections that even with mitigation, COVID-19 would kill as many as 240,000 people in the months ahead.

• What Fauci and his colleagues are trying to do is "flatten the curve" — taking steps now to reduce the speed of COVID-19's spread and therefore prevent hospital systems from being overloaded. The shape of that curve is the product of mathematical disease modeling.
• To simplify things — which is the point of a model — mathematical modelers take known data like the past number of cases, plug in estimates for things they don't know, and create models for how an outbreak will progress.

Yes, but: Any attempt to predict the future will be imperfect, and the less modelers know for certain, the more weight they need to put on their estimates. That's especially true for a new disease, so we've seen models spit out a wide range of potential outcomes for COVID-19 — sometimes by the same modelers.

• Early COVID-19 models by a team at Imperial College London assumed that demand for intensive care units would be roughly the same as previously modeled flu pandemics. The result was a milder forecast that initially encouraged the U.K. government to adopt a "mitigation" strategy with relatively little social distancing.
• But when data from China and Italy showed that COVID-19 patients required intensive care at much higher proportions, the same team revised its models, predicting that a laissez-faire strategy could lead to as many as 500,000 deaths in the U.K. alone.
• The British government eventually abandoned mitigation in favor of a near-total lockdown.

The problem is that politicians and mathematical modelers don't speak the same language and don't move at the same pace, especially during an outbreak.

• "Sometimes you need an immediate answer, not one in five days," says Caitlin Rivers, an epidemiologist and modeler at the Johns Hopkins Center for Health Security who earlier worked with the federal government.
• It's notable that while other governments publish data-rich updates on the epidemiology of COVID-19, the best dashboard in the U.S. is updated by academics at Johns Hopkins — not the U.S. government.

One possible solution is to try to bridge the gap between the mathematicians who model diseases and the politicians who respond to them.

• In a new report, Rivers and her colleagues suggest creating the equivalent of a National Weather Service for disease modeling. The proposed federal institution would provide authoritative disease models during outbreaks and embed modelers in the government so that both cultures can learn from each other.

"Right now with modeling the outbreak, it's as if we're building the plane as we're flying it. If it was already there, it would be much smoother and more effective.

The bottom line: Because there's so much we still don't know about COVID-19 — and because the field mostly remains an academic endeavor — the models that are being produced are likely flawed. But they remain the best intelligence we have in the war on the pandemic.

We know COVID-19 will fundamentally alter the world, but those changes may not be the ones you expect.

The big picture: While much of the focus has been on the rush to remote work in the early stages of the pandemic, the longer-term consequences of COVID-19 may have more to do with how we keep ourselves healthy than how we work.

We're likely only in the early stages of the pandemic, but that hasn't stopped experts — including yours truly — from opining on how COVID-19 will change the post-disease world.

• But we're almost certainly wrong, as futurist Amy Webb tells me. "Any time a new change is foisted upon us, very quickly there is a bias to thinking that the new present is the future. That is almost universally never the case."

Be smart: Webb is the founder of the Future Today Institute and one of the sharpest people working on forecasting tomorrow. So she was a logical person to ask about what we're getting wrong — and right — about COVID-19's consequences

Wrong: The idea that remote work is here to stay.

• Webb notes the shift to remote work started around 15 years ago when offices went to an open plan layout — only to find that productivity tanked. "Most societies are not set up to support the daily productivity tasks you need as a remote worker or student," she says.

Right: What will last is the shift to telemedicine and at-home diagnostics, as well as drone delivery.

• Webb notes that telemedicine has always been held back by a federalized system that largely regulates doctors state by state. But emergency measures around the pandemic have led to the loosening of those regulations, opening the door wider for telemedicine.
• The same conditions have created an opportunity for at-home diagnostics, as patients seek to test themselves for coronavirus and other conditions. Webb expects companies like Amazon to push devices that can monitor your health directly. "Imagine having a smart toilet that can do urinalysis and check for problems like excess glucose at home," she says.
• The pandemic is already snarling supply chains and the problem is only likely to get worse. But with streets empty because of social distancing, Webb sees this as the perfect moment for companies to test out autonomous drones for delivering vital goods to the quarantined. "This is the best time for the FAA to begin allowing drone-based deliveries to happen," she says.

The bottom line: The post-pandemic world will be one where more things come to us, whether via a screen or via a robot.

Artificial intelligence experts at Stanford University are calling for the creation of a task force to establish a National Research Cloud to aid American AI research.

Why it matters: Government support for basic science helped create the postwar American technological colossus. But the unique resource needs of advanced AI research demands a new approach to ensure the field isn't dominated by a few large, rich companies.

The research used to train advanced AI requires a great deal of two things: computational power and data.

• Google needed nearly $1.5 million in computational cycles to train its Meena chatbot announced earlier this year, while Facebook is able to tap its enormous user-generated dataset for its own AI research. It's impossible for most academic AI researchers to access anywhere near that much computational power or raw data.
• To open up AI research to a wider group of players, experts at the Stanford Institute for Human-Centered Artificial Intelligence want to create a public-private task force that will build what they call a National Research Cloud.

How it works: The National Research Cloud would seek to even the playing field by providing academic researchers affordable access to high-powered computational resources as well as access to datasets held in government agencies like Medicare and the VA.

• "If you're at a college like Kansas State, there's no way for you to do this research now," says John Etchemendy, the co-director of the Stanford center. "But if we have a real national push to provide that compute and data, in a privacy protective way, it would benefit society at large."

The bottom line: The only way to ensure the boons of AI research are spread widely is to eliminate the barriers to doing that work.

The coronavirus pandemic will leave its mark on urban centers long after the outbreak itself recedes, my Axios colleague Kim Hart writes.

Why it matters: The most densely populated cities are ground zero for the virus' rapid spread and highest death tolls — and they're also likely to be pioneers in making lasting changes to help prevent the same level of devastation in the future.

The big picture: The combination of urbanization, climate change and a hyper-connected society means infectious disease epidemics are likely to become more common, the World Economic Forum warns.

Reality check: In many cases, the COVID-19 outbreak will accelerate trends that were already underway. The new normal may, in fact, feel pretty normal.

Go deeper

This pandemic is exposing the futility of the national security state (Andrew Bacevich — The Nation)

• A former Army officer on the failure of the national security establishment to prepare for the threat of infectious disease.

This is not the apocalypse you were looking for (Laurie Penny — Wired)

• How postapocalyptic entertainment like "The Walking Dead" failed to prepare us for a disaster most of us are watching from our living rooms.

How likely are you to die from the coronavirus? (Tom Chivers — UnHerd)

• Let's face it — this is the question we all have. A smart science journalist walks us through why it's so hard to get the answer.

Pokémon, stay (James Poniewozik — New York Times)

• My former Time magazine colleague on playing a video game that takes you outside in the era of sheltering in place.

Social distancing measures around the world are so great they have actually caused the Earth to move less.

The big picture: There is no shortage of ways to measure how much responses to the pandemic have slowed human movement. But the idea that the planet itself has become stiller is truly mind-blowing.

In a story in Nature, Elizabeth Gibney reported that scientists are detecting a drop in seismic noise that could be due to the suspension of transportation and other forms of human movement.

• Noise reduction of this magnitude usually only occurs briefly during Christmas, when much of the world is on holiday.
• Data from a seismometer at the Royal Observatory of Belgium found social distancing measures had caused human-induced seismic noise to fall by a third in Brussels.

Why it matters: Beyond giving us one more sign of just how frozen in place the world has become, the drop in background noise should help city-based seismic detectors pinpoint the location of earthquake aftershocks. Which is good, just in case the Earth throws us another curveball.