Defined by What We Don't Know
We have seen coronaviruses before, but we still don’t know enough about COVID-19. We don’t have a set of drugs or therapies. We are only just beginning to identify why it affects people so differently. At the moment, it is like a rare disease; we haven’t seen enough cases to have a clear view of how it works and how to best treat it.
The current focus is on managing the sickest patients. For the moment, this focus is tactical. There isn’t time to slowly observe the evolution of the disease and perform quantitative analysis on the relative effectiveness of therapies in the way that we would normally run a clinical trial. We are rushing the customary research processes because the need is so urgent.
We also don’t understand the means of transmission or have a solid grasp of the way the disease evolves in different people. Just a few days ago, we were advising those who are healthy not to wear masks in public places unless you have symptoms. Then the CDC considered changing their guidance because we are now discovering that there are people who are carrying the disease but may never get sick. Now they are telling everyone to wear a mask anytime they leave the house. This rapid change in policy is an indication of an ever-changing amount of knowledge.
The COVID crisis is a litmus test of all our systems: our frontline patient-facing services, our logistics and supply chains, and the way that we perform medical research.
We are only starting to have enough data to start analyzing it, even though we know the data are flawed, we are working with what we have to identify the most effective treatments and develop a vaccine.
The data are flawed for a number of reasons. Initially, people were arriving in the hospital seriously ill and we didn’t know what it was because they had never seen it before. Some doctors in Italy have said that they are sure that they were seeing cases of COVID-19 last autumn. Testing kits haven’t been available, so patients arriving at the hospital were not able to be tested. The lack of early widespread testing and our uncertainty around the accuracy of the tests has made it hard. Because of the variety of testing methods that have been used, the data we have may be inconsistent.
The shortage of tests has meant we are testing only those that are ill, or we know have been exposed. We don’t have a good read on the number of people who carry the disease. We also don’t have any understanding of people in the general population that have antibodies to the disease in the general population. So, even though we have statistics around the number of cases and death rates by sectors of the population, it is safe to say that the data are useful but will ultimately be revised.
Once a pattern of symptoms emerged in China, the search for the cause got underway. Tests were developed and patients were tested. It was a while before testing got underway, and patients who recovered on their own or had returned home after treatment would not have been tested. Modelers at the University of Hong Kong have estimated the Chinese mainland probably had more than 230,000 cases (nearly 4 times the confirmed number). Add the fact that today we believe some people recover without ever showing symptoms, we know those numbers are probably still low.
There are many unknowns surrounding immunity, so while 2% to 6% of people exposed to the virus that causes the common cold will get sick, 20% to 60% of the people exposed to the COVID-19 will get sick. As we gain knowledge, we mitigate risk. The COVID-19 pandemic has been defined by what we don’t know—the eradication will be defined by what we learn.