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What policy makers are missing from coronavirus — data

At the moment, we are flying blind with data on only the reported number of cases and deaths. We could be making wrong decisions based on them.

Cape Cod Hospital has a testing tent for COVID-19 on site.John Tlumacki/Globe Staff

The novel coronavirus has upended life as we know it. Almost one-third of the global population is under lockdown, and almost two-thirds of Americans are now under orders to stay home until the end of April. Leaders in every country are struggling to find a balance between health and economic consequences of this pandemic. It remains unclear when we will regain any sense of normalcy. Dr. Anthony Fauci, director of the National Institute for Allergy and Infectious Diseases, has repeatedly said that he doesn’t have enough data to know when it might be safe to go back to work.

But what data do we need after witnessing around 1 million confirmed cases of COVID-19 globally and more than 49,000 deaths as of Thursday morning? In the United States alone, there have been more than 216,000 confirmed cases and more than 4,800 deaths. While state governments and the Centers for Disease Control and Prevention are focused on medical supplies and social distancing, we are currently missing two critical pieces of information that are easy to estimate to make better-informed decisions about the future.


We need to know the transmission rate of the coronavirus to estimate the number of future infections. We currently test only people who show symptoms and are really sick. A large proportion of the population may be infected, experience only mild or no symptoms, and are going untested. A recent study in Science magazine, estimated that prior to Jan. 23, 2020 in China, almost 86 percent of infections were undocumented. While it is understandable to limit testing to only sick patients, we need to know how far this infection has spread in the general population to make informed policy decisions.

Some experts have argued for mass testing to estimate this proportion, which isn’t feasible given the limited supply of testing kits. We can, however, estimate the rate of infection in the population by testing a random sample of about 1,000 people. We can even accommodate differences in infection rates across regions through stratified sampling, which would give greater weight to states with larger populations and higher uncertainty of infection rates. If this sampling were to be done over time, it would provide a clear picture of when the infection peak is likely to occur. It will also serve as an indicator of the effectiveness of government interventions such as stay-at-home or the quarantine of high-risk individuals. And it would enable better estimates of the true mortality rate, a parameter that would reduce anxiety among the broad population.


Given the limited supply of test kits, saving 1,000 kits for estimating infection rates may be viewed by some policy makers as a luxury we can’t afford in this crisis situation. But with current testing capacity at more than 100,000 tests per day, using 1 percent of kits is a minor cost to pay to obtain better estimates of infection and mortality rates. The estimates would inform us about the severity and duration of shutdowns needed to keep millions of people safe.

And we must know the proportion of people who have recovered from COVID-19 and are potentially immune to the virus. This includes a large number of asymptomatic people who were never tested. A recent study found that, within 15 days, recovered patients develop enough antibodies to become immune to the disease. As the proportion of immune patients increases, it may create herd immunity that protects against future spread of the virus and eventually reaches a tipping point to bend the curve. It is also unclear what proportion of survivors are truly immune. Recent reports from Wuhan, China, indicate that 5 to 10 percent of the recovered patients have tested positive again. Knowing the percent of recovered patients who are likely to be immune is crucial to predict when the pandemic will slow down and end and if we are likely to see its resurgence in the future.


Understanding immune patients is also useful for finding a potential cure for the disease. Doctors are investigating survivors’ plasma as a possible therapy, and the Food and Drug Administration recently approved this research. This would also require collaboration with other countries that have experienced this pandemic early on and have a large number of potentially immune people.

These two critical parameters — infection rate and immunity rate — are closely linked. Higher infection rate is bad because it can overwhelm our hospital systems, but it also increases the number of recovered and immune patients who in turn may create herd immunity and a faster end of the pandemic.

At the moment, we are flying blind, with data on only the reported number of cases and deaths. These numbers, by themselves, do not provide the vital information necessary to build suitable models for guiding rational policy decisions. The proposed random sampling to get good estimates of the two critical parameters is easy and quick to do. Without knowing them, we could be making wrong decisions. As in any decision, it is better to be approximately right than precisely wrong.


Sunil Gupta is a professor of business and cochair of Driving Digital Strategy at Harvard Business School.