• Labor market: In May 2019, about 5.9 million people were unemployed in a labor force of 163 million. In May 2020, the full count of the unemployed is about 30.2 million, which when divided by the May 2019 labor force, implies an effective unemployment rate of 18.6%

• GDP: By 2030, current projections foresee a cumulative loss in real GDP of $7.9 trillion

• New Infections: About 100,000 people in the United States are getting infected with the SARS-CoV-2 virus each day

• Currently Infected: 1.4 million people in the US

• Ever Infected: 16 million people (4.8% of the US population)

• Projected Deaths: The current projections for total deaths from Covid-19, the disease that the virus causes, are expected to be in the range 150,000 - 215,000 by October 1, 2020.

(Data updates, May 1)

In this post, I list a number of specific applications where tests could save lives and restore confidence. Tests could stop the deaths in nursing homes. They could make it safe for nurses to go back to their jobs. They could make parents feel safe taking their children to the clinic to get a vaccination. Such applications yield an immediate demand for 13.9 million tests per day.

At the current government reimbursement rate of $100 per test, the daily cost of these tests would be $1.39 billion. At this rate, the $11 billion that the Congress allocated to the states to support testing will be exhausted in 8 days.

Imagine a world in which the only way to get a soda is to get your doctor to write a prescription. It costs $20 per can. Your insurance company pays. The economy produces about 100,000 sodas each day.

If you lived in this world, do you think you could get people to scale up the production of soda to a level of millions of cans per day? It would be a challenge, but not because it is hard to produce and distribute soda.

America is confronting two crises: an economic crisis laying waste to our livelihoods and a health crisis threatening our lives. The twin crises are deeply intertwined: our economy cannot be reopened without credibly addressing fears of infection and resurgence.

How much difference does it make if the test used to send people into quarantine is bad? Not as much as you might think.

The simulated data here contrast policies that isolate people who test positive using four different assumptions about the quality of the test. Even a very bad test cuts the fraction of the population who are ultimately infected almost in half. And when I say bad, I mean bad – an 80% false negative rate, which means that 4 out of 5 of people who are truly infectious will get a negative test result – i.e. a result saying that they are not infectious.