By Michael Mina
The constant drumbeat of “we do not know that yet” is tiring. We know so much about SARS-CoV-2 and Covid-19. We knew it before this virus was ever discovered! We’ve watched since January with study after study reaffirming our expectations of this virus in so many ways.
In many ways, we got lucky on this front. Take HIV for example. HIV was a new virus for which we generally did have to rewrite the textbook. But this (corona)virus is different from HIV in that it is behaving in almost all ways per the “textbook”.
Transmission: We knew that this was a transmissible respiratory virus, yet when the virus began transmitting internationally, we said we weren’t sure if it would become global. Of course, it would when, after a few weeks, it had already transmitted to many countries in Asia and the Middle East.
We knew this is a respiratory virus, and yet we said we didn’t have enough information to know if masks help or hinder. This was always ridiculous.
Yes, there were reasons – for instance, conserving personal protective gear (PPE) – but stating that we didn’t know enough shouldn’t have happened. The lasting effects of that early messaging error persist today with an active anti-mask community.
Immunity: We know much about viral immunology. When antibodies started to be observed to wane, we said this is an otherworldly virus and thought we must learn everything about it anew. But that’s not so. It serves as a textbook example of immunity to an acute respiratory virus.
Antibodies go high with primary infection and wane quickly. We know this. Yet it led to massive confusion and concern. People like Akiko Iwasaki luckily chimed in to remind us that this is normal, and New York Times journalist Apoorva Mandavilli wrote a nice article about it.
Knowledge of immunity is accelerating because of this virus, yes. The important point is that this virus is not in isolation. It fits in most ways what we expect and know already. We don’t need entirely new empirical immunological evidence for every assertion.
Testing and transmissible virus. We’ve seen major confusion about PCR (polymerase chain reaction), the role of Ct (cycle threshold) values, the limitations of PCR and antigen tests, and whether tests can help understand the infection status of people.
Over and over, people say that we don’t yet know enough to know about how to use Ct values to help determine transmissibility. But we do! We know so much and have known so much. For instance, we know that low Ct values mean that someone is likely transmissible. We know that high Ct values are most likely to mean low viral loads.
At the very least on this front, we can use a low Ct value to say “this person definitely has a high viral load” because a bad swab isn’t going to add virus to the test. The other direction is not as clear. But at this particular point, that low Ct means high viral load need not be in question.
When we began to focus on the use of rapid tests, people said we don’t know enough. Rapid tests might miss everyone on the front end of an infection if they aren’t as sensitive as PCR. But we know how viruses like this grow.
Did we know the precise kinetics? No. But we know that viruses like this grow exponentially once they take off and become PCR detectable. And we know that they slow down and get cleared.
The earliest empirical data showed that the RNA (ribonucleic acid) gets cleared after many weeks or months even, and the earliest epidemiological data showed that people were largely transmitting virus for (in general) a maximum of about ten days.
So, we don’t need all of the empirical evidence to have a very good idea of viral trajectories and how Ct values correlate with transmissibility. We combined data sets and used statistical methods to do this with precision many months before the full kinetics curves started to become available.
Now that the empirical data are becoming available, it is proving to be in very strong agreement. Science and mathematical methods combined with understanding first principles of virus replication and transmission allowed us to do this.
It’s great to have the empirical data, but we didn’t need to learn it all over anew to start making decisions. Up to this day, too many people continue to say that we don’t have enough data to make informed decisions. This is just not true.
Seasonality. This is the last point. I recognize that we haven’t been with this virus for a full year and it’s not endemic, so it’s hard to know if it’s seasonal.
But we know so much about it already from its closest neighbours. It was reasonable to make the leap many months ago that it is most likely seasonal – and we should have planned for it. Largely, we did not, and once again our policy leaders are being taken off guard as a major upswing in cases is precisely what we are now seeing.
These leaps in these areas aren’t guesses. They are part assumptions, but it’s clear-cut, scientifically defined aspects of viruses like this that enable us to combine complex concepts/data and develop pictures of a virus like this before all of the empirical data exist.
When we can obtain the empirical data, great. But by going back to first principles and the basics about viral replication, cell biology, immunity, transmission, droplets, pandemic spread, etc., we often can infer a tremendous amount despite highly incomplete SARS-CoV-2 specific data.
To sum it all up, I hope that in this pandemic, when the pace of necessary forward movement outstrips the pace of data collection, we can recognize how much we already know. I am still looking for major aspects of this virus that do not fit the textbook. But they arise only very rarely. Most of the major pieces really do fit the textbook.
- Michael Mina, an epidemiologist, immunologist and physician with Harvard Public Health/Medical School, filed this report for Medium Coronavirus Blog.