The Rules of Contagion (Wellcome Collection)
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school of fish
‘if you’ve seen one pandemic, you’ve seen … one pandemic.’
From innovations to infections, epidemics almost inevitably slow down as susceptibles become harder to find.
Using public health approaches to prevent crime would be hugely cost-effective, both in the US and elsewhere
When there are enough immune people to prevent transmission, we say that the population has acquired ‘herd immunity’.
dengue and Zika are both spread by Aedes mosquitoes, best known for being stripey and lazy (‘aedes’ means ‘house’ in Latin).
herd immunity meant that the population as a whole could block transmission, even if some individuals were still susceptible.
one study estimated that for every ten US mass shootings, there are two additional shootings as a result of social contagion.[
Or as comedian John Oliver described it: ‘everything you don’t understand about money combined with everything you don’t understand about computers.’
emotional content. Berger and Milkman’s analysis found that having an element of surprise or practical value could also influence an article’s shareability.
‘The basic point was that all this integration did indeed reduce the probability of mini-crashes,’ Haldane said, ‘but increased the probability of a maxi-crash.’
Meanwhile further south, physicians Oswaldo Cruz and Carlos Chagas were spearheading anti-malaria programmes in Brazil, helping to reduce cases among construction workers.
‘Rarely have scientists engaged with a new research agenda with such a sense of urgency and from such a small knowledge base,’ wrote epidemiologist Laura Rodrigues in early 2016.[9]
Just as modern social scientists can’t make people take up smoking to see if the habit spreads, researchers in the 1950s couldn’t ask people to smoke to find out if it caused cancer.
Network scientists have found that, when examining the probability of a crisis, small errors in knowledge about the lending network could lead to big errors in estimates of system-wide risk.
‘Human beings have limited foresight and great imagination,’ financial mathematician Emanuel Derman once noted, ‘so that, inevitably, a model will be used in ways its creator never intended.’
According to carl bell, a public health specialist at the University of Chicago, three things are required to stop an epidemic: an evidence base, a method for implementation, and political will.
A closer look at the Colorado Springs data suggested that transmission was likely to be the result of delays in getting treatment among certain social groups, rather than an unusually high level of sexual activity.
He was now too far into his career to change direction; what use was mathematics to someone working in medicine? ‘It was the unfortunate passion of a married man for some beautiful but inaccessible lady,’ as he put it.
‘Epidemiology is in fact a mathematical subject,’ he wrote in 1911, ‘and fewer absurd mistakes would be made regarding it (for example, those regarding malaria) if more attention were given to the mathematical study of it.’[
From disease epidemics to terrorism and crime, forecasts can help agencies plan and allocate resources. They can also help draw attention to a problem, persuading people that there is a need to allocate resources in the first place.
If we have a promising theory, we therefore need to seek out examples that don’t fit. We need to work out where its limits are and what exceptions there might be, because even widely reported theories might not be as conclusive as they seem.
misdemeanours can explain a massive drop in violence. So what caused the decline? Economist Steven Levitt has argued that expanded access to abortion after 1973 played a role. His theory goes that this meant there were fewer unwanted children, who would
which reduce the probability of transmission during sex. In recent years, there has also been a lot of success with so-called pre-exposure prophylaxis (PrEP), whereby hiv-negative people take anti-hiv drugs to reduce their susceptibility to the infection.
In the decade since Christakis and Fowler published their initial analysis of the Framingham data, evidence for social contagion has accumulated. Several other research groups have also shown that things like obesity, smoking, and happiness can be contagious.
So what caused the decline? Economist Steven Levitt has argued that expanded access to abortion after 1973 played a role. His theory goes that this meant there were fewer unwanted children, who would have been more likely to be involved in crime when they grew up.
If high profile scientists dominate a field, it can hinder the growth of competing ideas. As a result, new theories may only gain traction once dominant scientists cede the limelight. As physicist Max Planck supposedly once said, ‘science advances one funeral at a time.’
One of the top threats was thought to be smallpox. Despite having been eradicated in the wild, samples of the virus were still stored in two government labs, one in the US and one in Russia. What if other, unreported, smallpox viruses were out there and fell into the wrong hands?
Behind these new signals lay the Zika virus, named after the Zika Forest in southern Uganda. A close relative of the dengue virus, Zika was first identified in the forest’s mosquitoes in 1947. In the local language, Zika means ‘overgrown’[5] and grow it would, from Uganda to Tahiti to Rio de Janeiro and beyond.
Widespread refusal of vaccination therefore tends to be a luxury, a side effect of living in places that – thanks to vaccination – have seen little of such infections in recent decades.[18] One 2019 survey found that European countries tended to have much lower levels of trust in vaccines compared to those in Africa and Asia.
Blaming certain groups for outbreaks is not a new phenomenon. In the sixteenth century, the English believed syphilis came from France, so referred to it as the ‘French pox’. The French, believing it to be from Naples, called it the ‘Neopolitan disease’. In Russia, it was the Polish disease, in Poland it was Turkish, and in Turkey it was Christian.
Ross would no doubt be glad to see how influential his ideas have been. Despite winning a Nobel Prize for his discovery that mosquitoes transmit malaria, he did not view this as his biggest achievement. ‘In my own opinion my principal work has been to establish the general laws of epidemics,’ he once wrote.[48] And he didn’t just mean disease epidemics.
As any teacher or parent will know, interactions with children means an increased risk of infection. In the US, people without children in their house typically spend a few weeks of the year infected with viruses; people with one child have an infection for about a third of the year; and those with two children will on average carry viruses more often than not.
According to Rogers, four different types of people are responsible for the growth of a product: initial uptake comes from ‘innovators’, followed by ‘early adopters’, then the majority of the population, and finally ‘laggards’. His research into innovations mostly followed this descriptive approach, starting with the S-curve and trying to find possible explanations.
Researchers found increasing evidence of a link between Zika infection and neurological conditions: as well as gbs, Zika seemed to lead to pregnancy complications. The main concern was microcephaly, where babies develop a smaller brain than usual, resulting in a smaller skull.[6] This can cause a host of serious health issues, including seizures and intellectual disabilities.
In fact, a recent review found that, in total, academics have proposed twenty-four different explanations for the decline in US crime during the 1990s.[93] These theories have attracted plenty of attention – as well as criticism – but the researchers involved all acknowledge that it’s a complicated question. In reality, the drop in crime was likely the result of a combination of factors.[94
Some people had even developed treatments for malaria, despite not knowing what caused the disease. In the fourth century, Chinese scholar Ge Hong described how the qinghao plant could reduce fevers. Extracts of this plant now form the basis for modern malaria treatments.[16] (Other attempts were less successful: the word ‘abracadabra’ originated as a Roman spell to ward off the disease.[17]
China might be far away for a crow, but it’s relatively close for a human. It turns out that the spread of flu in 2009 is much easier to explain if we instead define distances according to airline passenger flows. And not just flu: sars followed similar airline routes when it emerged in China in 2003, arriving in countries like the Republic of Ireland and Canada before Thailand and South Korea.
But easy access to deadly methods can make a difference for what are often impulse decisions. In 1998, the UK switched from selling paracetamol in bottles to blister packs containing up to thirty-two tablets. The extra effort involved with blister packs seemed to deter people; in the decade after the packs were introduced, there was about a 40 per cent reduction in deaths from paracetamol overdoses.
1998, when mathematicians Duncan Watts and Steven Strogatz developed the concept of a ‘small-world’ network, in which most links were local but a few were long-range. They found that such networks cropped up in all sorts of places: the electricity grid, neurons in worm brains, co-stars in film casts, even Erdős’s academic collaborations.[57] It was a remarkable finding, and more discoveries were about to follow.
Unfortunately, outlets often ignore these guidelines. Researchers at Columbia University noted a 10 per cent rise in suicides in the months following the death of comedian Robin Williams.[14] They pointed to a potential contagion effect, given that many media reports about Williams’ death did not follow who guidelines, and the largest increase in suicides occurred in middle-aged men using the same method as Williams.
It turned out that articles that triggered an intense emotional response were more likely to be shared. This was the case both for positive emotions, such as awe, and negative ones like anger. In contrast, articles that evoked so-called ‘deactivating’ emotions like sadness were shared less often. Other researchers have found a similar emotional effect; people are more willing to spread stories that evoke feelings of disgust, for example.
It would take many more years for mosquito control to be widely adopted. Ross would not live to see the most dramatic reductions in malaria cases: the disease remained in England until the 1950s, and was only eliminated from continental Europe in 1975.[29] Although his ideas eventually started to catch on, he lamented the delay. ‘The world requires at least ten years to understand a new idea,’ he once wrote, ‘however important or simple it may be.’
R therefore depends on four factors: the duration of time a person is infectious; the average number of opportunities they have to spread the infection each day they’re infectious; the probability an opportunity results in transmission; and the average susceptibility of the population. I like to call these the ‘DOTS’ for short. Joining them together gives us the value of the reproduction number: R = Duration × Opportunities × Transmission probability × Susceptibility
Brendan Nyhan and Jason Reifler proposed that persuasion can suffer from a ‘backfire effect’. They’d presented people with information that conflicted with their political ideology, such as the lack of weapons of mass destruction in Iraq before the 2003 war, or the decline in revenues following President Bush’s tax cuts. But it didn’t seem to convince many of them. Worse, some people appeared to become more confident in their existing beliefs after seeing the new information.
In 2007, physician Nicholas Christakis and social scientist James Fowler published a paper titled ‘The Spread of Obesity in a Large Social Network over 32 Years’. They had studied health data from participants in the long-running Framingham Heart Study, based in the city of Framingham, Massachusetts. As well as suggesting that obesity could spread between friends, they proposed that there could be a knock-on effect further into the network, potentially influencing friends-of-friends and friends-of-friends-of-friends.
Viewing at-risk people as special or different can encourage a ‘them and us’ attitude, leading to segregation and stigma. In turn, this can make epidemics harder to control. From hiv/aids to Ebola, blame – and fear of blame – has pushed many outbreaks out of view. Suspicion around disease can result in many patients and their families being shunned by the local community.[74] This makes people reluctant to report the disease, which in turn amplifies transmission, by making the most important individuals harder to reach.
The idea of such influencers was inspired by psychologist Stanley Milgram’s famous ‘small-world’ experiment. In 1967, Milgram set three hundred people the task of getting a message to a specific stockbroker who lived in the town of Sharon, near Boston.[4] In the end, sixty-four of the messages would find their target. Of these, a quarter flowed through the same one person, who was a local clothing merchant. Milgram said it came as a shock to the stockbroker to find out that this merchant was apparently his biggest link to the wider world.
The most prolific mathematician in history was an academic nomad. Paul Erdős spent his career travelling the world, living from two half-full suitcases without a credit card or chequebook. ‘Property is a nuisance,’ as he put it. Far from being a recluse, though, he used his trips to accumulate a vast network of research collaborations. Fuelled by coffee and amphetemines, he’d turn up at colleagues’ houses, announcing that ‘my brain is open’. By the time he died in 1996, he’d published about 1,500 papers, with over eight thousand co-authors.
There is a long-standing paradox in medicine: people who have a heart attack or stroke while surrounded by relatives take longer to get medical care. This may well be down to the structure of social networks. There’s evidence that close-knit groups of relatives tend to prefer a wait-and-see approach after witnessing a mild stroke, with nobody willing to contradict the dominant view. In contrast, ‘weak ties’ – like co-workers or non-relatives – can bring a more diverse set of perspectives, so flag up symptoms faster and call for help sooner.
This idea isn’t only useful for diseases. During the mid-2000s, Jonah Peretti and Duncan Watts applied the same method to marketing campaigns. It meant they could get at the underlying transmissibility of an idea, rather than just describing what a campaign had looked like. In 2004, for example, anti gun violence group The Brady Campaign had sent out e-mails asking people to support new gun control measures. They encouraged recipients to forward the e-mails to their friends; some of these friends th <Você alcançou o limite de recortes para este item>
That’s the aim of this book. By exploring contagion across different areas of life, we’ll find out what makes things spread and why outbreaks look like they do. Along the way, we’ll see the connections that are emerging between seemingly unrelated problems: from banking crises, gun violence and fake news to disease evolution, opioid addiction and social inequality. As well as covering the ideas that can help us to tackle outbreaks, we’ll look at the unusual situations that are changing how we think about patterns of infections, beliefs, and behaviour.
We face a paradox when it comes to forecasting outbreaks. Although pessimistic weather forecasts won’t affect the size of a storm, outbreak predictions can influence the final number of cases. If a model suggests the outbreak is a genuine threat, it may trigger a major response from health agencies. And if this brings the outbreak under control, it means the original forecast will be wrong. It’s therefore easy to confuse a useless forecast (i.e. one that would never have happened) with a useful one, which would have happened had agencies not intervened.
soon faded. To ensure the research was completely transparent, Brookman and Kalla published all the data and code behind the analysis. It provided an optimistic epilogue to what had been an awkward few years for the research community. With the right approach, it was possible to change attitudes that many had believed were deeply ingrained. It showed that views don’t necessarily spread in the way we assume they do, nor are people as fixed as we think they might be. When faced with apparent hostility, it seems there can be a lot to gain by trying something new.
According to evolutionary biologist Nuno Faria and his colleagues, neither theory was particularly good.[45] Based on the genetic diversity of Zika viruses circulating in Latin America by 2016, they reckoned that the infection was introduced much earlier than previously thought. The virus probably hit the continent in mid-to-late 2013. Although too early for the canoe championship or World Cup, the time range coincided with the Confederations Cup, a regional football tournament held in June 2013. What’s more, French Polynesia was one of the countries competing.
Successful crime reduction can come in a variety of forms. In 1980, for example, West Germany made it mandatory for motorcyclists to wear helmets. Over the next six years, motorcycle thefts fell by two thirds. The reason was simple: inconvenience. Thieves could no longer decide to steal a motorcycle on the spur of the moment. Instead, they’d have to plan ahead and carry a helmet around. A few years earlier, the Netherlands and Great Britain had introduced similar helmet laws. Both had also seen a massive drop in thefts, showing how social norms can influence crime rates.
The first Cure Violence project started in 2000, in West Garfield Park in Chicago. Why did they pick that location? ‘It was the most violent police district in the country at the time,’ Slutkin said. ‘It has always been my bias – as it is for many epidemiologists – to head for the middle of the epidemic, because it’s your best test and you can affect the greatest impact.’ One year after the programme started, shootings in West Garfield Park had dropped by about two thirds. The change had been rapid, with interrupters breaking the chains of violence from one person to another.
These papers have been hugely influential. In the decade after it was published, the obesity study alone was cited over 4,000 times, with many seeing the research as evidence that such traits can spread. But it’s also come under fire. Soon after the obesity and smoking studies were published, a paper in the British Medical Journal suggested that Christakis and Fowler’s analysis might have flagged up effects that weren’t really there.[25] Then mathematician Russell Lyons wrote a paper arguing that the researchers had made ‘fundamental errors’ and that ‘their major claims are unfounded’.
The small-world idea had addressed the issue of clustering and long-range links, but physicists Albert-László Barabási and Réka Albert spotted something else unusual about real-life networks. From film collaborations to the World Wide Web, they’d noticed that some nodes in the network had a huge number of connections, far more than typically appeared in the Erdős–Rényi or small-world networks. In 1999, the pair proposed a simple mechanism to explain this extreme variability in connections: new nodes that joined the network would preferentially attach to already popular ones.[58] It was a case of the ‘rich get richer’.
So how did the patient zero designation come about? In the original outbreak investigation, Dugas hadn’t actually been listed as ‘Patient 0’, but rather as ‘Patient O’, the ‘O’ short for ‘Outside California’. In 1984, William Darrow, a researcher with the Centers for Disease Control and Prevention (CDC), had been assigned to investigate a cluster of deaths among gay men in Los Angeles.[66] The CDC generally gave each case a number based on the order in which they had been reported, but the cases had been relabelled for the LA analysis. Before Dugas had been linked to the Los Angeles cluster, he was simply ‘Patient 057’.
Researchers at Facebook and Cornell University had wanted to explore how emotions spread online, so they’d altered people’s News Feeds for a week and tracked what happened. The team published the results in early 2014. By tweaking what people were exposed to, they found that emotion was contagious: people who saw fewer positive posts had on average posted less positive content themselves, and vice versa. In hindsight, this result might seem unsurprising, but at the time it ran counter to a popular notion. Before the experiment, many people believed that seeing cheerful content on Facebook could make us feel inadequate, and hence less happy.
Our understanding of contagion has advanced dramatically in recent years, and not just in my field of disease research. With detailed data on social interactions, researchers are discovering how information can evolve to become more persuasive and shareable, why some outbreaks keep peaking – like the 2009 flu pandemic did – and how ‘small-world’ connections between distant friends can help certain ideas spread widely (and yet hinder others). At the same time, we’re learning more about how rumours emerge and spread, why some outbreaks are harder to explain than others, and how online algorithms are influencing our lives and infringing on our privacy.
Why doesn’t one strain end up dominating everywhere? Our social behaviour probably has something to do with it. If people gather into distinct tight-knit cliques, it can allow a wider range of strains to linger in a population. In essence, each strain can find its own home territory, without having to constantly compete with others.[11] Such social interactions would also explain the huge diversity in ideas and opinions online. From political stances to conspiracy theories, social media communities frequently cluster around similar worldviews.[12] This creates the potential for ‘echo chambers’, in which people rarely hear views that contradict their own.
In 1974, David Phillips published a landmark paper examining media coverage of suicides. He found that when British and American newspapers ran a front-page story about a suicide, the number of such deaths in the local area tended to increase immediately afterwards.[12] Subsequent studies have found similar patterns with media reports, suggesting that suicide can be transmitted.[13] In response, who have published guidelines for responsible reporting of suicides. Media outlets should provide information about where to seek help, while avoiding sensational headlines, details about the method involved, and suggestions that the suicide was a solution to a problem.
Illustration of an outbreak curve that grows exponentially until everyone is affected Back in real life, there are a few infections that affect their hosts in a way that increases transmission. Animals infected with rabies are often more aggressive, which helps the virus to spread through bites,[55] and people who have malaria can give off an odour that makes them more attractive to mosquitoes.[56] But such effects generally aren’t large enough to overcome declining numbers of susceptibles in the later stages of an epidemic. What’s more, many infections have the opposite effect on behaviour, causing lethargy or inactivity, which reduces the potential for transmission.
Such blame can stick for a long time. We still refer to the 1918 influenza pandemic, which killed tens of millions of people globally, as the ‘Spanish flu’. The name emerged during the outbreak because media reports suggested Spain was the worst hit country in Europe. However, these reports weren’t quite what they seemed. At the time, Spain had no wartime censorship of news reports, unlike Germany, England and France, who quashed news of disease for fear that it might damage morale. The media blackout in these countries therefore made it appear that Spain had far more cases than anywhere else. (For their part, the Spanish media tried to blame the disease on the French.
Another person to join the Federal Reserve discussions was Robert May, who had previously supervised Sugihara’s PhD. An ecologist by training, May had worked extensively on analysis of infectious diseases. Although May was drawn into financial research largely by accident, he would go on to publish several studies looking at contagion in financial markets. In a 2013 piece for The Lancet medical journal, he noted the apparent similarity between disease outbreaks and financial bubbles. ‘The recent rise in financial assets and the subsequent crash have rather precisely the same shape as the typical rise and fall of cases in an outbreak of measles or other infection,’ he wrote.
effective as it seemed? If people are rational, we might expect them to update their beliefs when presented with new information. In scientific research this approach is known as ‘Bayesian reasoning’. Named after eighteenth-century statistician Thomas Bayes, the idea is to treat knowledge as a belief that we have a certain level of confidence in. For example, suppose you are strongly considering marrying someone, having thought carefully about the relationship. In this situation, it would take a very good reason for you to change your mind. However, if you’re not totally sure about the relationship, you might be persuaded against marriage more easily. Something that might seem trivial to the infatuated
Many species have to adapt simply to keep pace with their competitors. After humans came up with antibiotics to treat bacterial infections, some bacteria evolved to become resistant to common drugs. In response, we turned to even stronger antibiotics. This put pressure on bacteria to evolve further. Treatments gradually became more extreme, just to have the same impact as lesser drugs did decades earlier.[38] In biology, this arms race is known as the ‘Red Queen effect’, after the character in Lewis Carroll’s Through the Looking-Glass. When Alice complains that running in the looking-glass world doesn’t take her anywhere new, the Red Queen replies that, ‘here, you see, it takes all the running you can do, to keep in the same place.’
Scientific papers aren’t only relevant to scientists. Ed Catmull, co-founder of Pixar, has argued that publications are a useful way of building links with specialists outside their company.[8] ‘Publishing may give away ideas, but it keeps us connected with the academic community,’ he once wrote. ‘This connection is worth far more than any ideas we may have revealed’. Pixar is known for encouraging ‘small-world’ encounters between different parts of a network. This has even influenced the design of their building, which has a large central atrium containing potential hubs for random interactions, like mailboxes and the cafeteria. ‘Most buildings are designed for some functional purpose, but ours is structured to maximize inadvertent encounters,’ as Catmull put it.
I’ve found that people generally respond to mathematical analysis in one of two ways. The first is with suspicion. This is understandable: if something is opaque and unfamiliar, our instinct can be to not trust it. As a result, the analysis will probably be ignored. The second kind of response is at the other extreme. Rather than ignore results, people may have too much faith in them. Opaque and difficult is seen as a good thing. I’ve often heard people suggest that a piece of maths is brilliant because nobody can understand it. In their view, complicated means clever. According to statistician George Box, it’s not just observers who can be seduced by mathematical analysis. ‘Statisticians, like artists, have the bad habit of falling in love with their models,’ he supposedly once said.
When sociologists at Duke University got US volunteers to follow Twitter accounts with opposing views, they found that people tended to retreat further back into their own political territory afterwards.[25] On average, Republicans became more conservative and Democrats more liberal. This isn’t quite the same as the ‘backfire effect’ we saw in Chapter 3, because people weren’t having specific beliefs challenged, but it does imply that reducing political polarisation isn’t as simple as creating new online connections. As in real life, we may resent being exposed to views we disagree with.[26] Although having meaningful face-to-face conversations can help change attitudes – as they have with prejudice and violence – viewing opinions in an online feed won’t necessarily have the same effect.
In February 2009, investor Warren Buffett used his annual letter to shareholders to warn about the ‘frightening web of mutual dependence’ between large banks.[87] ‘Participants seeking to dodge troubles face the same problem as someone seeking to avoid venereal disease,’ he wrote. ‘It’s not just whom you sleep with, but also whom they are sleeping with.’ As well as putting supposedly careful institutions at risk, Buffett suggested that the network structure could also incentivise bad behaviour. If the government needed to step in and help during a crisis, the first companies on the list would be those that were capable of infecting many others. ‘Sleeping around, to continue our metaphor, can actually be useful for large derivatives dealers because it assures them government aid if trouble hits.’
In 2003, Watts and his colleagues at Columbia re-ran Milgram’s experiment, this time with e-mails and on a much larger scale.[5] Picking eighteen different target individuals across thirteen countries, the team started almost 25,000 e-mail chains, asking each participant to get their message to a specific target. In Milgram’s smaller study, the clothing merchant had appeared to be a vital link, but this wasn’t the case for the e-mail chains. The messages in each chain flowed through a range of different people, rather than the same ‘influencers’ cropping up again and again. What’s more, the Columbia researchers asked participants why they forwarded the e-mail to the people they did. Rather than sending the message to contacts who were especially popular or well connected, people tended to pick based on characteristics like location or occupation.
Some diseases have a relatively low R. For pandemic flu, R is generally around 1–2, which is about the same as Ebola during the early stages of the 2013–16 West Africa epidemic. On average, each Ebola case passed the virus onto a couple of other people. Other infections can spread more easily. The sars virus, which caused outbreaks in Asia in early 2003, had an R of 2–3. Smallpox, which is still the only human infection that’s been eradicated, had an R of 4–6 in an entirely susceptible population. Chickenpox is slightly higher, with an R around 6–8 if everyone is susceptible. Yet these numbers are low in comparison to what measles is capable of. In a fully susceptible community, a single measles case can generate more than 20 new infections on average.[42] Much of this is down to the incredible lingering power of the measles virus: if you sneeze in a room when you have the infection, there could still be virus floating around in the air a couple of hours later.[43] As
We often share characteristics with people we know, from health and lifestyle choices to politicial views and wealth. In general, there are three possible explanations for such similarities. One is social contagion: perhaps you behave in a certain way because your friends have influenced you over time. Alternatively, it may be the other way around: you may have chosen to become friends because you already shared certain characteristics. This is known as ‘homophily’, the idea that ‘birds of a feather flock together’. Of course, your behaviour might be nothing to do with social connections at all. You may just happen to share the same environment, which influences your behaviour. Sociologist Max Weber used the example of a crowd of people opening umbrellas when it starts to rain. They aren’t necessarily reacting to each other; they’re reacting to the clouds above.[33] It can be tough to work out which of the three explanations – social contagion, homophily or a shared environment – is the correct one.
When Ross returned to India, he set out to test the idea, with an experiment that would be unlikely to pass a modern ethics board.[18] He got mosquitoes to feed on an infected patient then lay eggs in a bottle of water; once the eggs had hatched, he paid three people to drink the water. To his disappointment, none of them got malaria. So how did the parasites get into people? Ross eventually wrote to Manson with a new theory, suggesting that the infection might spread through mosquito bites. The mosquitoes injected some saliva with each bite: maybe this was enough to let the parasites in? Unable to recruit enough human volunteers for another study, Ross experimented with birds. First, he collected some mosquitoes and got them to feed on the blood of an infected bird. Then he let these mosquitoes bite healthy birds, which soon came down with the disease as well. Finally, he dissected the saliva glands of the infected mosquitoes, where he found malaria parasites. Having discovered the true route of transmission, he realised just how absurd their previous theories had been. ‘Men and birds don’t go about eating dead mosquitoes,’ he told Manson.
If people are rational, we might expect them to update their beliefs when presented with new information. In scientific research this approach is known as ‘Bayesian reasoning’. Named after eighteenth-century statistician Thomas Bayes, the idea is to treat knowledge as a belief that we have a certain level of confidence in. For example, suppose you are strongly considering marrying someone, having thought carefully about the relationship. In this situation, it would take a very good reason for you to change your mind. However, if you’re not totally sure about the relationship, you might be persuaded against marriage more easily. Something that might seem trivial to the infatuated may be enough to tip a wavering mind towards a break-up. The same logic applies to other situations. If you start with a firm belief, you’ll generally need strong evidence to overcome it; if you are unsure at first, it might not take much for you to change your opinion. Your belief after exposure to new information therefore depends on two things: the strength of your initial belief and the strength of the new evidence.[54] This concept is at the heart of Bayesian reasoning – and much of modern statistics.
Why do complex contagions occur? Damon Centola and his colleague Michael Macy have proposed four processes that might explain what’s happening. First, there can be benefits to joining something that has existing participants. From social networks to protests, new ideas are often more appealing if more people have already adopted them. Second, multiple exposures can generate credibility: people are more likely to believe in something if they get confirmation from several sources. Third, ideas can depend on social legitimacy: knowing about something isn’t the same as seeing others acting – or not acting – on it. Take fire alarms. As well as signaling there might be a fire, alarms make it acceptable for everyone to leave the building. One classic 1968 experiment had students sit working in a room as it slowly filled with fake smoke.[49] If they were alone, they would generally respond; if they were with a group of studious actors, they would continue to work, waiting for someone else to react. Finally, we have the process of emotional amplification. People may be more likely to adopt certain ideas or behaviours amid the intensity of a social gathering: just think about the collective emotion that comes with something like a wedding or a music concert.
We can therefore use the reproduction number to work out how many people we need to vaccinate to control an infection. Suppose an infection has an R of 5 in a fully susceptible population, as smallpox did, but we then vaccinate four out of every five people. Before vaccination, we’d have expected a typical infectious person to infect five other people. If the vaccine is 100 per cent effective, four of these people will now be immune on average. So each infectious person would be expected to generate only one additional case. Comparison of transmission with and without 80 per cent vaccination, when R is 5 in a fully susceptible population If we instead vaccinate more than four fifths of the population, the average number of secondary cases will drop below one. We’d therefore expect the number of infections to decline over time, which would bring the disease under control. We can use the same logic to work out vaccination targets for other infections. If R is 10 in a fully susceptible population, we’d need to vaccinate at least 9 in every 10 people. If R is 20, as it can be for measles, we need to vaccinate 19 out of every 20, or over 95 per cent of the population, to stop outbreaks. This percentage is commonly known as the ‘herd immunity threshold’.
If we want to avoid country-specific disease names, it helps to suggest an alternative. One Saturday morning in March 2003, a group of experts gathered at who headquarters in Geneva to discuss a newly discovered infection in Asia.[77] Cases had already appeared in Hong Kong, China and Vietnam, with another reported in Frankfurt that morning. who was about to announce the threat to the world, but first they needed a name. They wanted something that was easy to remember, but which wouldn’t stigmatize the countries involved. Eventually they settled on ‘Severe Acute Respiratory Syndrome’, or sars for short. The sars epidemic would result in over eight thousand cases and several hundred deaths, across multiple continents. Despite being brought under control in June 2003, the epidemic would cost an estimated $40 billion dollars globally.[78] It wasn’t just the direct cost of treating disease cases; it was the economic impact of closed workplaces, empty hotels and cancelled trade. According to Andy Haldane, now Chief Economist at the Bank of England, the wider effects of the sars epidemic were comparable with the fallout from the 2008 financial crisis. ‘These similarities are striking,’ he said in a 2009 speech.[79] ‘An external event strikes. Fear grips the system, which, in consequence, seizes. The resulting collateral damage is wide and deep.’
Although long-distance flight connections are important for introducing viruses to new countries, travel within the US is dominated by local movements. The same is true of many other countries.[22] To simulate these local movements, researchers often use what’s known as a ‘gravity model’. The idea is that we are drawn to places depending on how close and populous they are, much like larger, denser planets have a stronger gravitational pull. If you live in a village, you might visit a nearby town more often than a city further away; if you live in a city, you’ll probably spend little time in the surrounding towns. This might seem like an obvious way to think about interactions and movements, but historically people have thought otherwise. In the mid-1840s, at the peak of Britain’s railway bubble, engineers assumed that most traffic would come from long-distance travel between big cities. Unfortunately, few bothered to question this assumption. There were some studies on the continent, though. To work out how people might actually travel, Belgian engineer Henri-Guillaume Desart designed the first ever gravity model in 1846. His analysis showed that there would be a lot of demand for local trips, an idea that was ignored by rail operators on the other side of the channel. The British railway network would probably have been far more efficient had it not been for this oversight.
The who study was the result of Watts and her colleagues applying public health ideas to the issue of domestic violence. ‘A lot of previous research treated it as a police issue or focused on psychological drivers of violence,’ she said. ‘Public health people ask, “What’s the big picture? What does the evidence say about individual, relationship and community risk factors?”’ Some have suggested that domestic violence is completely context or culture specific, but this isn’t necessarily the case. ‘There are some really common elements that consistently come out,’ Watts said, ‘like exposure to violence in childhood.’ In most of the locations in the who study, at least one in four women had previously been physically abused by a partner. Watts has noted that violence can follow what’s known in medicine as a ‘dose-response effect’. For some diseases, the risk of illness can depend on the dose of pathogen a person is exposed to, with a small dose less likely to cause severe illness. There’s evidence of a similar effect in relationships. If a man or woman has a history involving violence, it increases the chance of domestic violence in their future relationships. And if both members of the relationship have a history of violence, this risk increases even further. This isn’t to say that people with a history involving violence will always have a violent future; like many infections, exposure to violence won’t necessarily lead to symptoms later on. But like infectious diseases, there are a number of factors – in our backgrounds, in our lifestyles, in our social interactions – that can increase the risk of an outbreak.[9]
Unfortunately, the mortgage models had some major flaws. Perhaps the biggest problem was that they were based on historical house prices, which had risen for the best part of two decades. This period of history suggested that the mortgage market wasn’t particularly correlated: if someone in Florida missed a payment, for example, it didn’t mean someone in California would too. Although some had speculated that housing was a bubble set to burst, many remained optimistic. In July 2005, CNBC interviewed Ben Bernanke, who chaired President Bush’s Council of Economic Advisers and would shortly become Chairman of the US Federal Reserve. What did Bernanke think the worst-case scenario was? What would happen if house prices dropped across the country? ‘It’s a pretty unlikely possibility,’ Bernanke said.[7] ‘We’ve never had a decline in house prices on a nationwide basis.’ In February 2007, a year before Bear Stearns collapsed, credit specialist Janet Tavakoli wrote about the rise of investment products like CDOs. She was particularly unimpressed with the models used to estimate correlations between mortgages. By making assumptions that were so far removed from reality, these models had in effect created a mathematical illusion, a way of making high-risk loans look like low-risk investments.[8] ‘Correlation trading has spread through the psyche of the financial markets like a highly infectious thought virus,’ Tavakoli noted. ‘So far, there have been few fatalities, but several victims have fallen ill, and the disease is rapidly spreading.’[9] Others shared her skepticism, viewing popular correlation methods as an overly simplistic way of analysing mortgage products. One leading hedge fund reportedly kept an abacus in one of its conference rooms; there was a label next to it that read ‘correlation model’.
Clustering is common with other types of violence too. In 2015, a quarter of US gun murders were concentrated in neighborhoods that made up less than 2 per cent of the country’s overall population.[17] When Gary Slutkin and his colleagues set out to tackle violence as if it were an outbreak, it was neighbourhoods like these that they planned to target. They called the initial programme ‘CeaseFire’; this would later evolve into a larger organisation called Cure Violence. In those early days, it took a while to work out precisely what approach they should use. ‘We took five years of strategy development before we put a single thing on the street,’ Slutkin said. The Cure Violence method would end up having three parts. First, the team hires ‘violence interrupters’ who can spot potential conflicts and intervene to stop the transmission of violence. Someone might end up in hospital with a gunshot wound, for example, and an interrupter will step in to talk their friends out of a retaliatory attack. Second, Cure Violence identifies who is at greatest risk of violence, using outreach workers to encourage a change in attitudes and behaviour. This can include help with things like job hunting or drug treatment. Finally, the team works to change social norms about guns in the wider community. The idea is to have a range of voices speaking out against a culture of violence. Interrupters and outreach workers are recruited directly from the affected communities; some are former criminals or gang members. ‘We hire workers who are credible with that population,’ said Charlie Ransford, Cure Violence’s Director of Science and Policy. ‘To change people’s behaviour and talk them out of doing something it helps if you have an understanding of where they’re coming from, and they feel like you have an understanding and maybe even know you or know someone who knows you.’[