The question came up earlier today when Dr. Sara Cody broke the news about this shelter-in-place business lasting a lot longer than we first expected.
At what point can we ease up on these social distancing mandates?
Santa Clara County’s public health chief said that all depends.
“I think what we’d need to see is that our demand curve—which is how many people are ill requiring hospitalization and ICU care—comes to a place where it is comfortably nestled under our supply curve,” Cody replied at the tail end of this afternoon’s press conference. “What’s the supply? Beds, ICUs, necessary staff, necessary equipment to care for [patients] in the way that they need to be cared for. So, it’s a complex balance.”
Thankfully, researchers at Stanford University has been hard at work exploring the complexities of that balance and what would happen if we dial down restrictions prematurely. A team led by biologist Erin Mordecai has developed an interactive tool that shows how COVID-19 would spread with various interventions, such as social distancing.
For example, what would happen if Dr. Cody lifted her shelter-in-place order now? How long do we have to keep it up before we start to see a drop in confirmed cases? What non-pharmaceutical measures should we take to buy time until a vaccine gets developed?
The goal of the Stanford project is to help the public understand just how important it is to “flatten the curve” or delay the peak of that now-ubiquitous epidemiological graph.
“Many of our health resources have a fixed capacity,” an intro on the project explains. “If we have too many cases at a time, we simply won’t be able to take care of everyone who gets sick. Hospitalizations above our limit will mean making hard decisions about which patients to prioritize (for example, Santa Clara County has roughly 4,500 hospital beds). Some people may not be able to receive care. We are already seeing this scenario play out in places like Italy. The more social distancing we practice, the flatter the curve will be.”
Here’s a link to the online tool, if you’re inclined to play around with it. And below is a Q&A that Stanford Woods Institute for the Environment spokesman Rob Jordan conducted with Mordecai about the project.
What do your models do?
Our models explore interventions that change over time. For example: What happens if we wait one week longer before issuing a shelter in place order? How long do we expect a given percent reduction in social contacts to need to be sustained before we start to see a decline in cases? How can we use adaptive strategies that actively turn off and on interventions as we track the number of hospitalized cases?
What are some of the main takeaways of your project?
Our models suggest that beginning interventions early—before the epidemic has grown too large in a given community—is far more important than precisely how much we cut down on social contacts. It makes clear that if we impose social distancing for a short or medium time period—several weeks to months – and then lift restrictions altogether, we expect to see a resurgence of disease transmission because many people will still be susceptible.
We saw this during the 1918 flu pandemic when many U.S. cities lifted their restrictions after 3-8 weeks and saw large second peaks of flu transmission. That pandemic eventually infected about a third of the world population and killed 50 million people. To avoid a resurgence of COVID-19, we need to apply multiple interventions over a long period of time—12 to 18 months or more—until effective treatments and/or vaccines are widely available.
Did you model any promising alternatives to strict, long-term restrictions?
We don’t need to be totally locked down for a year or more. Adaptive strategies that actively turn on and off interventions—like a light-switch—can allow for periods of greater mobility while still keeping the epidemic at levels our healthcare system can manage. Improved testing capacity will allow us to use more targeted approaches to identify and isolate infected people and their social contacts.
How has your work on other diseases informed how you think about COVID-19?
We tried to use what we’ve thought about a lot—mosquito-borne disease transmission—and apply it to a non-mosquito-borne virus.
There’s kind of a canonical way of modeling infectious disease dynamics that a lot of people take across a wide range of different diseases, which involves taking your human population and dividing it into classes of people who are fully susceptible to disease; people who have been exposed but not necessarily sick yet or able to transmit the disease yet; people who are sick and transmitting the disease; and people who have recovered.
What’s really cool is that these simple models seem to be really successful at describing the onset and progression of a wide range of infectious diseases, from flu to plague.
We’ve used those types of models for mosquito-borne diseases a lot. With COVID-19, it’s actually a slightly simpler type of model than what you would use for a mosquito-borne disease because you’re only having to track the human population.
Are there other criteria you could imagine adding to your interactive model over time?
At a minimum, we plan to update parameters as more data become available and other scientists continue to make better estimates. We will update our baseline intervention scenarios to reflect those that are actually implemented, and may expand the interventions we consider if new plans are proposed. Ideally, we would eventually be able to fit our model to infection dynamics in specific locations so that user-chosen scenarios better reflect the future of COVID-19 in those areas.
On a related note, it might be interesting to follow what’s going on in China and South Korea now that they’re starting to see declines in cases and returning to a more social interaction. Will they see a second peak? I think that will be really informative for the rest of the world.