BI: What would you say to people who might see the idea of spreading out infections over time as counterintuitive?
DH: The step we want is containment. Ideally, when a new virus pops up, we should contain it—don’t allow it to spread anywhere; stop it completely. That would be perfect. Then you don’t have to worry about it. But containment can fail for a variety of reasons, especially with a virus that is easily transmitted, like this one. So, when containment fails, you go to mitigation.
There are two goals of mitigation: one is to prevent further spread to other areas. We don’t want to overwhelm the response systems. That’s the reason I published the graphic with the line representing our health-care capacity. And it’s important to remember, it’s not just the hospitals. It’s the people who run the power plants, the people who help move food around this country—all the vital services we have in our society depend on people doing this work. We haven’t replaced them with robots yet. People are not expendable. They are necessary. If too many people don’t show up to work because they are sick or fearful of catching the disease, then our whole society will grind to a halt. So, we want to make sure we are not overwhelmed.
Best case: containment. Second case: let’s just spread it out so this thing can be managed over a longer period of time.
BI: Does spreading the disease out mean we get fewer infections and deaths overall?
DH: It could mean that. That is the hope. Another reason for spreading the disease out over time? It gives us time to come up with a vaccine. Pandemics like this tend to travel in waves. They can move around the globe for years. That’s what the 1918 flu did. It came back in subsequent years because people became complacent and stopped all the things they were doing to prevent the spread of the flu in the first place. There were vulnerable people out there who didn’t get the flu the first time. But we now have the ability to create vaccines to shut this down, because ultimately what we want is an immune population. If enough people catch this, the population is immune. It’s what we call herd immunity. When you have enough people in a population who are immune to a disease, then it cannot spread.
BI: Has this chart ever played out with real numbers in history?
DH: There’s one real example in the 1918 flu pandemic. We have examples of three cities: Philadelphia, St. Louis, and Denver. Each experienced a flu outbreak in different ways. In Philadelphia, officials decided not to cancel a bond parade to raise money for World War I. That resulted in up to 200,000 people in the city at the beginning of the flu spread. Public health people warned that this was not a good idea, but the parade went on anyway, and within 48 hours, people around the region began to die. The death rate was very high, and systems were overwhelmed.
On the other hand, St. Louis had a strong public health official who locked down the city, told people to socially isolate, and did all the things necessary to ensure the flu didn’t spread. St. Louis had a much flatter curve. What I describe abstractly in that chart they were able to do in reality in St. Louis.
Denver essentially did what St. Louis did, but then let the lid off the pot too early, and the city saw a second peak in cases, sort of like stopping chemo early and the cancer comes back quick.
BI: What do you wish people knew about preventing the spread of disease from a public health perspective?
DH: When we’re effective and people heed our warnings and the disease does not spread as we expected or anticipated, nothing happens. Then it looks like our warnings were too hysterical and people say, "Oh, you public health people, you always exaggerate. It wasn’t that bad." Yeah, it wasn’t that bad because people heeded the warnings and did what they were supposed to do. That’s what I call the public-health conundrum. When you’re effective, nothing happens.
The views expressed in this interview are those of the interviewee and do not necessarily represent those of HMH.
Teach the “Flattening the Curve” Chart
Provide students with a list of words and phrases related to COVID-19, such as pandemic, quarantine, isolation, lockdown, community spread, and social distancing. For each, have them write a definition and draw a picture that shows the meaning.
Analyze the Chart
Show students the chart and ask them to describe what they see. Ask: What do the two curves represent? Why is one curve steeper than the other? What is happening with the first curve in relation to the health-care system? What is happening with the second curve in relation to the health-care system? Point out that the second curve is spread out over a longer period of time than the first curve. How does that extended time benefit the health-care system?
Compare the Pandemics
What do COVID-19 and the flu pandemic of 1918 have in common? Read about them and discover key similarities and differences. Share your findings in a comparison chart. Download the student resource here.
Model an Epidemic
Challenge students to model an epidemic moving through a community. Give them these criteria:
- On Day 0, 10 people are infected.
- Each day, the infection rate increases by 30%.
Have students model the epidemic through 5 days, using circle or tick marks to represent the number of people infected. Ask: How many people do you think will be infected after two weeks? (394 people) Challenge them to graph the two-week period in Excel or another program. Explain that the graphs show the rate of infection of COVID-19 rising exponentially.
Demonstrate the difference between exponential and linear growth by showing students the graph below. The yellow line shows the two-week rate of infection described above. The blue line shows a two-week rate of infection increasing by 3 each day after the initial 10 infections on Day 0. Have students compare the graphs. How are they alike? How are they different? What is the difference between exponential and linear growth? What would have to happen in order for the yellow line to start curving downward? Be sure students justify their responses using the graph.