Interview Highlights: Projecting the Future to Tackle COVID-19 Today with Jeffrey Shaman

Cover illustration and gif by: Zoe Chan (CC '22)

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Interview by: Makena Binker Cosen (CC '21)

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Jeffrey Shaman, PhD, is an infectious disease modeler, a Professor of Environmental Health Sciences and the Director of the Climate and Health Program at the Mailman School of Public Health of Columbia University.

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The following is a heavily condensed version of the full interview. If you're interested, read more here.


Could you describe your COVID-19 research at the moment?

Our team has been running models that project COVID-19 outcomes for New York City, U.S. counties, and the entire country, as well as for China. For example, back in January, we started by estimating how many people had undocumented infections and how contagious they were. 

Our goal is to understand the pandemic’s epidemiological characteristics: its distribution, patterns, and determinants of health across populations. This type of information can help us identify what public health interventions different places ought to adopt.

What’s the difference between a prediction and a projection?

When you make a prediction, you’re assuming that certain behaviors stay constant. For example, weather forecasts assume that hurricanes can’t be disrupted, just as flu forecasts assume that people are going to adopt typical precautions to stop the flu from spreading, but no more than that.

There are so many unknowns right now that making a prediction is impossible. We are leading very different lives than we were formerly. We have completely disrupted our economy and shut down businesses and schools. We don’t know what people are going to do in the future or how much loosening restrictions on non-essential businesses will actually affect the number of opportunities for the virus to spread.  
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If a particular location allowed non-essential businesses to re-open, how many would actually do so? Some may choose not to open because they consider it unsafe for their employees or their customers; or because it isn’t economically viable for them to work in a partially open economy. Even if they do re-open, how many customers will frequent those businesses? Will people enforce social distancing and wear masks? 

Projections allow us to lay out different scenarios. One scenario might assume that there is a five percent increase in the transmissibility of the virus on a weekly basis, as 

people get more relaxed, businesses open, and there is less compliance with social distancing and face-mask wearing. Another scenario might be that the transmissibility will stay constant, if everyone stays on their toes. Modeling these scenarios allows us to identify the scope of possible outcomes and to understand how sensitive the virus is to specific interventions. In turn, this helps us determine which measures we ought to enforce in different places.​​


What biases do you face in data collection?

First off, not everybody is tested. Many COVID cases go undocumented, and deaths are undercounted. In fact, someone from a hospice in rural Georgia reached out to me, saying that they used to get one or two sepsis cases each year. Now everybody there is dying of “sepsis” and none of them have been tested for COVID. It’s likely that they’re all COVID patients, just not known to be.

​In some places, the increase in mortality during the pandemic has been higher than we expected it to be due to COVID-19. That means that we don’t know what is causing that excess mortality. It may be because hospitals are overrun by COVID and can’t provide the same services they used to. Then again, just as you might expect that more traffic injuries might be going untreated, you might also expect that there have been less traffic accidents since less people are out on the road. Essentially, we don’t know what this excess mortality represents, but we still consider COVID deaths as undercounted. 

On top of this, there are changes in reporting and testing practices over time. We don’t have access to information on the microscale processes that might be supporting or not supporting transmission locally. 


How do your models address these biases?

Our job is to use models that explicitly allow and account for biases in the data. Observations are not the Gospel. There is some unknown truth out there that we’re trying to estimate better by using both these models and the observations in conjunction with one another. 

Because there’s a lengthy lag between someone acquiring the infection and them being counted as a confirmed case, our main blind spot is that we don’t know what happened over the last ten days. We update our projections twice a week using new information from the last three or four days, and we also change our scenarios as we consider new behavioral changes. 

It’s really important in this situation to be very responsive. When there are outbreaks taking place, we would like to see states and communities responding aggressively. Unfortunately, that has not been the case.


What are some key interventions that you’ve seen lead to an effective response to COVID?

There are a handful of countries that have done a good job responding to large outbreaks, such as Vietnam, Taiwan, Thailand, and South Korea. China has been able to handle it in a very different, authoritarian way. I would argue that countries near the epicenter that have already dealt with SARS and MERS were more prepared to address COVID than other countries. Still, other places like Germany, Israel, New Zealand, and Iceland are doing well too.

One of the best examples to look at is South Korea, since it is a densely populated, modern country well-connected with the world economy, and people fly through it all the time.

• They aggressively developed tests when this virus emerged there, which was the same day it emerged in the United States. They had their pharmaceutical companies develop a lot of diagnostic tests that were then verified by government labs and flooded into the market, giving ample opportunity for anybody who really wanted to be tested to get tested. 
• They already had laws in the books that allowed them to use people’s credit cards, video surveillance, and phone GPS records to conduct contact tracing to control the virus.
• They had passed a quarantine act that allows them to enforce quarantine for individuals who have been in contact with someone who is infectious. 
• They shut their schools down, but not their businesses. That’s not to say that their businesses didn’t take a big hit initially -- no one was going to them comparatively. But, with high compliance in mask usage, they really managed to squash the virus. 

Since then, they’ve further reopened businesses, houses of worship, and nightclubs, among other non-essential services. The opening of nightclubs was associated with a cluster of outbreaks, around forty cases a day. 

Now, if they had forty cases per day for a thousand days, that would be forty thousand cases. If we assume there are about ten times as many infections overall as there are cases, which is probably a high number for South Korea considering how well they test, that would be 400,000 people infected in the next three years, roughly. That is less than one percent of their total population.

In their new normal, with a reasonable economy, maintaining their unemployment rate at four percent, South Korea could hold on for three years while the world tries to develop an effective vaccine or therapeutic treatment for COVID-19. What’s more, they could deploy it before 99% of their population is ever infected with the virus.

Now, South Korea has a little bit more than one-seventh of the population the United States has. Forty cases per day there would approach 280 cases per day here. Right now, the United States has about 20,000 cases per day. We’re nowhere near their numbers and we’ve disrupted our economy and skyrocketed unemployment. We’re not alone. A lot of countries have done the same. 

Right now, we seem to be ignoring the fact that this can take off exponentially again. We may be catching a break in summer because the virus may be somewhat less transmissible then. However, if we’re really complacent about this and we’re not willing to reimpose shelter-in-place methods, this virus will run over us.


Has your team modeled how protests and the response to protests may influence COVID-19 outcomes?

As of this interview (6/10/20), it’s too early to tell. They’ve only been going on for twelve days and that’s about the length of time between somebody acquiring an infection and being confirmed as a case. We haven’t had enough time to see the results, just as with the loosening of restrictions.

Many factors may help preclude virus transmission at protests, like wearing masks and protesting during daytime, outdoors. In the open air, where the wind whisks things away and UV radiation from sunshine breaks down the virus, the risk may be slight.
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On the other hand, bringing people together may provide more opportunities for transmission and there have been locations where people are not wearing face masks at all. That’s more concerning. This is not to diminish the need for the protests. That is a choice and these issues are very important, equally important, if not more important, and they are connected to public health issues as well.