How deadly is the coronavirus? It is a simple but vital question that we don’t know the answer to right now. With American lives and livelihoods on the line, we need a science-based baseline from which to make public policy decisions. Hopefully those answers come sooner than later as the White House looks to do random sampling, something I recently reported

To be clear, every single life has value, and the overburdening of hospitals in places such as New York City is real and devastating. The toll on our doctors and nurses, many of whom have contracted the coronavirus by selflessly putting their own lives on the line to save others, is also real. We mourn the loss of each precious life and are in debt to the heroes on the front line.

The economic toll of shutting down nonessential businesses across the country is also real. A record-shattering 10 million Americans filing for unemployment in just two weeks and the largest bailout in United States history — $2.2 trillion — are sobering numbers that reflect the economic calamity we are facing. As government and public health officials make decisions of enormous magnitude, shouldn’t we know how infectious and lethal the coronavirus is?

That is why random sampling is important. John Ioannidis, a Stanford epidemiologist who is famous for debunking bad research, has been pushing for it. He told me that random sampling is needed and could be done with a couple of thousand tests. When I told him that I previously worked in the polling industry, he put it in terms that resonated with me. He said, “Random representative testing is like polling. We run thousands of opinion polls in this country. We should similarly get a representative sample of the population and get them tested. It is just so easy.”

A recent television interview with Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases and member of the White House Coronavirus Task Force, underscores the need. After estimating that 100,000 to 200,000 Americans could die of the coronavirus, he said that projections are a “moving target” and that models are “only as good and as accurate as your assumptions.” But how good are models if the data is insufficient? 

Ioannidis warned of a potential evidence fiasco in a recent op-ed for Stat. He wrote, “The data collected so far on how many people are infected and how the epidemic is evolving are utterly unreliable. Given the limited testing to date, some deaths and probably the vast majority of infections due to SARS-CoV-2 are being missed.”

Eran Bendavid and Jay Bhattacharya, also professors at Stanford, echoed that concern in The Wall Street Journal, writing, “The true fatality rate is the portion of those infected who die, not the deaths from identified positive cases.” They speculate that due to how infectious the coronavirus appears to be, and because tens of thousands of people traveled from Wuhan to America in December, millions of Americans could have been infected.

Random sampling will tell us what percentage of the population has the coronavirus and its lethality. Only testing the very sick skews mortality rates and leaves us in the dark about how many Americans are unknowingly walking around asymptomatic or with mild symptoms. Looking at other countries’ data also has its challenges; age structures, climate differences, quality of health care systems and testing all vary.

The elimination of red tape and point-of-care innovations by companies such as Abbott Labs have made testing easier and more readily available. To date, the United States has administered more than 1 million tests. That means we now have the capacity to test those in need and to get better data. 

With so much at stake, we need to have the best data possible through random sampling — and it needs to be done now.