[00:00:21]
SAM SCARPINO: I think in most places, we don’t have a great picture of what’s going on with respect to the coronavirus. There are some states in the U.S. that are running enough tests that we get a reasonable picture of the outbreak. There are some countries that are running enough tests that we’re getting a reasonable picture of the outbreak. But in large parts of the world, in large parts of the U.S., we’re really not testing enough and not testing in the right ways to understand exactly what’s going on with the outbreak. And certainly, that also translates into the mortality estimates. Also – you know, related to that point, we’re seeing increasing use of these antibody sero-surveillance studies to look retrospectively at who was infected. And because of the sort of haphazard way in which either the studies are being done or they’re being communicated, it’s difficult to translate those into the kinds of population-level estimates that we really want and need for our situational awareness around COVID. So I think we have a long ways to go in terms of improving the surveillance for this disease.
[00:01:36]
SAM SCARPINO: One of the things that I look for in the models is agreement or disagreement. And if you’re seeing two or three different models all pointing in the same direction, if you’re seeing the empirical data all pointing in the same direction, then you’re starting to feel better about trusting the overall pattern. So, for example, in Massachusetts, we see the percent positive on a seven-day rolling average coming down. We see the ICU bed count coming down. We see the models all projecting – you know, three or four different models all projecting that the peak in cases was probably last week or the week before, and the peak in ICU is kind of this week or last week. And so you have three or four different models, three or four different data points that are all agreeing with each other.
Other parts of the U.S., we see lots of disagreement between the models. And then, what you really need to do is kind of try to dig in and understand what is it about the model that is causing it to disagree with a different model. And oftentimes, what you’ll need to do is talk to individuals that have some expertise in these models – and ideally, individuals that maybe are not directly involved in building them so that you can get kind of an external perspective. But it’s difficult. It’s very difficult because our brains struggle with uncertainty. The models are built in very, very different ways. The data that feeds the models are often very different from each other, and so it’s just a – it is a really big challenge. And that’s not to be pessimistic. It’s so that people understand that if they’re not understanding what’s going on, it’s really because it’s a hard thing to wrap your heads around. And that’s in part because we need to do – you know, we need to increase our effort around in science communication in communicating the models. But it’s just, in general, a difficult thing for us to wrap our heads around.
[00:03:37]
SAM SCARPINO: At all levels, from the individual deciding whether they’re going to go to the grocery store today or not, to hospitals that are managing surges in ICU, to political leaders that are coming up with plans for reopening, there’s uncertainty. And that uncertainty in the number of cases, in the mortality rate, et cetera, translates into uncertainty in how risky any particular strategy is going to be. How much is – how much are we putting the public at risk by reopening barbershops and salons? How much am I putting myself and my family at risk by going outside without a mask? How much am I putting the hospital at risk by not having a mobile test center out in front of the hospital?
One of the things that we all need to do is pay close attention to the best current knowledge of the disease. Because this is a novel pathogen, we are learning about it in real time. It’s why the mask-wearing recommendations have changed. And that means that although our risk has always been high, we now understand that it’s high and that people need to be wearing masks when they’re outside. Similarly with the mobile test centers, we understand that people are going to flood the emergency rooms trying to get tested. And we need to do everything we can to redirect those individuals out of the emergency rooms, both for their protection and for the protection of the health care workers. In terms of reopening, we may start to reopen and then see that the number of cases are going up.
And so leaders need to have plans for, what do we do when the number of cases starts to go up? What are the scenarios in which we’re considering around reopening? And I think for a disease like COVID that is so severe and moves so quickly, I believe that we need to be erring on the side of caution around our own personal measures that we’re taking, around the measures that we’re taking from a political sort of population, city, state level. And part of that is because there are large fractions of the population either that have essential jobs or that are underrepresented with respect to their voices in the government or that are at risk for other reasons because of comorbidities or because of an underprivileged status. Those people maybe can’t work from home or don’t have a home to go to or don’t have the food that they need or the diapers that they need.
And as a result, all of us that have the privilege to stay home, that have the privilege to wear masks, that can physical distance, we need to take that privilege so that we provide some measure of protection to the rest of the population. And so I think with this decision-making under uncertainty, we need to realize that we’re learning in real time. We need to plan for us to be wrong about things and redirect course. We need to be very cautious because of how severe this situation is. And we need to be very aware of how our decisions affect the rest of the community that we live with, especially those that are most at risk.
