The COVID-19 pandemic was some epidemiologist’s nightmare when Adam Kucharski was writing Rules of Contagion. Released this week, the book, which includes brief mentions of the encroaching COVID-19 storm, draws on ideas from “outbreak science” to illuminate how and why viruses spread. Information from biology, Kucharski expertly demonstrates, has helped scientists understand how misinformation rages like fire in the fields of politics and finance. Kucharski is entitled to feel like Nostradamus, but people in his field “always have the next pandemic on the radar,” he told Wired.
Kucharski is an associate professor at the London School of Hygiene and Tropical Medicine. He is a mathematician by training, and like most hotshot quants, he planned on a career in investment banking. In the summer of 2008, he worked in finance, just as the global economy was teetering on the edge of collapse. Then he switched direction and became an epidemiologist. But Kucharski believes the two professions aren’t so different, at least for those who care about mathematical modeling. He says calculating and quantifying the curve of a pandemic is crucial to any successful campaign to end it.
Kucharski believes the same general principles of contagion apply in various parts of our lives—disease, financial bubbles, gun violence, even new ideas. Just as diseases spread when there are plenty of unsuspecting bodies to infect, so do financial schemes take off and crackpot ideas go viral. Then, inevitably, they decline and fade away. We all know this, intuitively, but scientists like Kucharski are working to build models to predict these trends, which can then be used to develop strategies to stop a contagion. Or, in some cases, to use the same principles to disseminate good ideas and healthier lifestyles. I caught up with Kucharski last week to learn about the key ideas in Rules of Contagion, and hear his latest views on the COVID-19 outbreak. He believes we’re still in the early stages—not even the mid-point—of this global pandemic.
Why has the COVID-19 pandemic been so difficult to slow down?
It can transmit easily between people. This isn’t a virus like MERS that caused fairly limited clusters and small numbers of sustained transmission events. Also, it’s the way in which it transmits. A lot of transmission seems to occur when people have mild symptoms, or even before they have symptoms, which means by the time you identify a clear case of COVID, you’re already playing catch-up with the outbreak.
Is it clear what we need to do to stop the pandemic?
The standard public health approach of identifying people with symptoms, working out who they’ve come in contact with, quarantining those people to try and break these chains of transmission, can be effective. Because of the infectious nature of the virus, it has to be quick and thorough to work. A lot of countries have introduced additional physical distancing measures, infection control, and in some cases full shutdowns. A lot of these targeted measures work best when numbers are fairly small because you can commit resources. When you start talking about thousands of infections, as in this case, that becomes much harder.
You are both an epidemiologist and a mathematician by training. Why are mathematical models so helpful in understanding pandemics, including COVID-19?
Mathematical models are a useful way of laying out the knowledge we have about an infection, and laying out the assumptions we can make about transmission. We look at the magnitude of transmission from person to person. We capture a reproduction number, the average number of new cases generated by an existing case. We also look at time scale. It’s the time between one person showing symptoms and the person they infect. These two things together give you the amount of growth at each step, and then the other tells you how quickly those steps are occurring.
You can get a biological virus from one person. But an idea might not take until several people have told you about it.
As you look at those two factors and look at COVID-19, what do you see?
That people can infect others very early in their infectious period means that that time scale can be quite short. We know that one case can infect two or three others, even if everyone is back to normal and behaving as they were. That means you can get an epidemic that potentially is doubling every three or four days. There’s potential for super-spreading events. You can get these large exposure events, where a lot of people suddenly become infected in a workplace or in a bar, and that means your outbreak really does accelerate. Certainly in all the epidemics I’ve faced, this is the toughest. This is something that’s unique in the last 100 years.
Would a widely available vaccine end the pandemic?
Not necessarily. The hope is we’ll have a vaccine as we do for many viruses that trigger a strong immune response that protects people from infection, and protects them from spreading it. But it may be the case that it’s either not effective in doing that, doesn’t fully stop transmission, or it doesn’t fully reduce the risk of infection. Also, obviously, we have to vaccinate at a much higher proportion of our population. That’s the more pessimistic end of the scale.
Is there an optimistic end of the scale?
We do need to be optimistic about what we can achieve, the improvements we can potentially make, what science might be able to deliver. But we have to be realistic about the range of possibilities. One thing that’s always struck me about this is the time scales we’re looking at. You can impose a lockdown, you can get cases down, but then you’ve still got an infection circulating, if not within your country, then within a lot of neighboring countries and countries you’re going to want to visit. That’s just going to be an ongoing problem.
How can we stop this pandemic?
Ideally, through an effective vaccine. But in some areas of the world we may see outbreaks that are uncontrolled, building some natural immunity, in which case this becomes an endemic virus. Or we see a number of countries stamp out local transmission and keep strict border restrictions over time. But if it’s circulating globally, as soon as you start to allow travel again, you’re going to have that risk of incoming cases again and again.
In your new book, you say there are parallels between contagions of disease and other kinds of contagion, including the 2008 financial disaster. What were the rules of that contagion?
One is a network feature where you have loops. Imagine you have a network where you can only get infected through one connection. In that way, it’s much easier to manage your risk. If you keep an eye on that connection, if you understand where the infection might be coming from, that’s something you can manage. If you have a network with a lot of hidden loops and hidden connections, it’s much harder to understand how the risk is going to be spread. You might not be connected to a specific bank that’s in trouble, but others might be, and then you might be connected to them. So you’ve got all these hidden loops through which you are going to be exposed.
Another characteristic is what we call a disassortative network. The highly connected big banks were connected to loads of smaller counter-parties. That meant you ended up with an outbreak that was slower, initially, but once it took off, once it spread in the network, it affected a huge number of institutions because you had large hubs that distributed the risk and contagion very far.
In all the epidemics I’ve faced, this is the toughest. This is something that’s unique in the last 100 years.
Misinformation also spreads like wildfire. Why?
There are a couple of features. One is the emotional response it triggers. A lot of studies have shown that things that trigger fundamental emotions like anger tend to spread faster. That can be traced back to perhaps evolutionary reasons—those kind of strong emotions are seen as important information. There can also be a local effect, particularly with vaccine information. In the United Kingdom, there was the now-debunked study about the MMR measles vaccine causing autism. The focus on MMR in the U.K. didn’t really spread to other countries at the time it came out. But the online environment has changed so that now local concerns become internationalized much faster, and people can build momentum around these views, which perhaps wouldn’t have been possible 20 years ago.
Suppose Facebook or Twitter hired you to stop the spread of false information. What would you do?
In the past, people had a sense that you can just remove all the harmful bad stuff online. That’s the equivalent saying about a disease, “Let’s just find all the cases and then we’d solve the problem.” Of course, you can’t do that for infectious disease. People know that you have to try and reduce the opportunities for it spreading. Pinterest, a few years ago, acknowledged that it wasn’t possible to get rid of absolutely every piece of harmful information, but they could change how people might be exposed to it. Similarly, WhatsApp made changes to their system about how much sharing could happen. One thing that’s striking about the coronavirus outbreak is how many platforms are now presenting preemptive information. If you search for COVID or coronavirus, you will preemptively be exposed to more reliable information sources. That could be viewed as an attempt to reduce susceptibility.
How does that preemptive process work?
The aim for contagion is to reduce susceptibility. You don’t have to interfere with the structure of the network, you don’t have to interfere with people’s interactions. There’ve been a few studies showing that if you can expose people to reliable information sources, or you can give them quick access to better information, they won’t be coming fresh to misinformation. The difficulty with political information is platforms have been far more reluctant to fact-check things and issue public corrections. Compare that with coronavirus, which has clear health information sources. I think we’ve seen a far more dramatic intervention in trying to make sure people have good information.
We’ve been talking about contagion as something terrible, but good ideas can also come to public attention. How do new ideas take off?
People put a lot of value on things that are new and that are useful. They communicate things related to survival and values. There’s been a lot of nice studies looking at the spread of stories and fairytales, at how aspects of them reflect the values of the society in which they’re spreading.
The appeal of “influencers” to marketing is obvious. But it’s something that doesn’t really hold up.
How do you get good ideas to spark?
One of the big distinctions between a biological virus and an idea is you can get a biological virus from one person. But an idea might not fully take until several people have told you about it. That means that the networks you need to get those kind of innovations to take off is different. You might not be able to tell your idea to one person and it’ll cause an outbreak. You might need to build local amplification within a company, within certain groups, and build on that momentum with links that enable it to spread more widely.
What role do so-called “influencers” play?
The appeal, certainly from a marketing point of view, is people, who are not necessarily celebrities, can, through word of mouth, spread things widely. That could be a much more cost-efficient strategy. But it’s something that doesn’t really hold up. There have been studies showing some people are slightly more influential than others, all things being equal, but really a lot of the ideas that do take off in a major way tend to be through high profile, highly connected people. The interesting aspect to this, which we’ve seen emerge in recent years, is trying to work out the balance. Do you get one high-profile celebrity to talk about your product, or do you try and spark ideas more widely through a network? It goes back to the idea about the network structure. Even if you get one or two high profile people, it might not reach that far in the network. But if you can seed it more widely, and in some cases, seed it randomly, that can be better than just trying to target a few high profile people.
One person you write about is Jonah Peretti. When he was a grad student in the MIT Media lab, he basically mastered the art of making an idea go viral. What did he do?
While he was a student, he created a huge amount of attention when he tried to order some personalized trainer sneakers from Nike with the word “Sweatshop” printed on them. He then got into this email exchange with the company. He ended up forwarding the exchange to some people, and it forwarded on and on, went viral. The media started picking it up, started amplifying it. He went to work for a startup that gave him freedom to try and create contagious content. He played around with what makes something spread, and he found generating controversy, and jumping on news stories, were some of the motivating factors that cause people to spread stuff. He had a lot of success doing it and ended up co-founding Buzzfeed, putting a lot of his ideas into practice.
It makes me wonder if we can spread good news or spread happiness, spread positivity. Can we do that?
I’d like to think we can. Strong emotions on the other side, like wonder and awe—which could be in a story about cool science—might spread very widely. Often when people see something horrible online, they want to comment on it, and they want to retweet it, with a comment. In doing that, they’re only amplifying and creating engagement metrics for that bit of content. But all of us, by understanding that process of contagion, can improve our interactions with things online, and in doing so, be part of that process that enables healthier use of our platforms.
Steve Paulson is the executive producer of Wisconsin Public Radio’s nationally syndicated show To the Best of Our Knowledge. He’s the author of Atoms and Eden: Conversations on Religion and Science. You can subscribe to TTBOOK’s podcast here.
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