Unfortunately, Hudson and Ross’s work on the Theory of Happenings would be limited to three papers. One barrier was the First World War. In 1916, Hudson was called away to help design aircraft as part of the British war effort, work for which she would later get an OBE.[62] After the war, they faced another hurdle, with the papers ignored by their target audience. ‘So little interest was taken in them by the “health authorities,” that I have thought it useless to continue,’ Ross later wrote.
When Ross first started working on the Theory of Happenings, he’d hoped it could eventually tackle ‘questions connected with statistics, demography, public health, the theory of evolution, and even commerce, politics and statesmanship’.[63] It was a grand vision, and one that would eventually transform how we think about contagion. Yet even in the field of infectious disease research, several decades would pass before the methods became popular. And it would take even longer for the ideas to make their way into other areas of life.
2
Panics and pandemics
‘I can calculate the motion of heavenly bodies but not the madness of people.’ According to legend, Isaac Newton said this after losing a fortune investing in the South Sea Company. He’d bought shares in late 1719 and initially seen his investment rise, which persuaded him to cash in. However, the share price continued to climb and Newton – regretting his hasty sale – reinvested. When the bubble burst a few months later, he lost £20,000, equivalent to around £20 million in today’s money.[1]
Great academic minds have a mixed record when it comes to financial markets. Some, like mathematicians Edward Thorp and James Simons, have set up successful investment funds, bringing in huge profits. Others have succeeded in sending money the opposite way. Take the hedge fund Long Term Capital Management (LTCM), which suffered massive losses following the Asian and Russian Financial Crises in 1997 and 1998. With two Nobel Prize-winning economists on its board and healthy initial profits, the firm had been the envy of Wall Street. Investment banks had lent them increasingly large sums of money to pursue increasingly ambitious trading strategies, to the point that when the fund went under in 1998, they had liabilities of over $100 billion.[2]
During the mid-1990s, a new phrase had become popular among bankers. ‘Financial contagion’ described the spread of economic problems from one country to another. The Asian Financial Crisis was a prime example.[3] It wasn’t the crisis itself that hit funds like LTCM; it was the indirect shockwaves that propagated through other markets. And because they’d lent so much to LTCM, banks also found themselves at risk. When some of Wall Street’s most powerful bankers gathered on the tenth floor of the Federal Reserve Bank of New York on 23 September 1998, it was this fear of contagion that brought them there. To avoid LTCM’s woes spreading to other institutions, they agreed a $3.6bn bailout. It was an expensive lesson, but unfortunately not one that was learned. Almost exactly ten years later, the same banks would be having the same conversations about financial contagion. This time it would be much worse.
I spent the summer of 2008 thinking about how to buy and sell the statistical concept of correlation. I’d just finished my penultimate year of university, and was interning with an investment bank in London’s Canary Wharf. The basic idea was simple enough. Correlation measures how much things move in line with each other: if a stock market is highly correlated, stocks will tend to rise or fall together; if it’s uncorrelated, some stocks might go up while others go down. If you think stocks are going to behave similarly in future, you’d ideally want a trading strategy that profited from this correlation. My job was to help develop such a strategy.
Correlation isn’t just some niche topic to keep a mathematically minded intern occupied. It turns out to be crucial for understanding why 2008 would end with a full-blown financial crisis. It can also help explain how contagion spreads more generally, from social behaviour to sexually transmitted infections. As we’ll see, it’s a link that would eventually pull outbreak analysis into the heart of modern finance.
Each morning that summer, I took the Docklands Light Railway to work. Just before it reached my stop at Canary Wharf, the train would pass the skyscraper at 25 Bank Street. The building was home to Lehman Brothers. When I’d applied for internships in late 2007, Lehman had been one of the coveted destinations for many applicants. It was part of the elite ‘bulge bracket’ group of banks, which also included firms like Goldman Sachs, JP Morgan, and Merrill Lynch. Bear Stearns had been part of the club too, until its collapse in March 2008.
Bear, as the bankers called it, had gone under because of failed investments in the mortgage market. Soon after, JP Morgan bought the carcass for less than a tenth of its earlier value. By the summer, everyone in the industry was speculating on which firm would go under next. Lehman seemed to be top of the list.
For mathematics students, an internship in finance was the brightly lit path that distracted from all others. Everyone I knew on my degree course, regardless of their eventual career, signed up for one. I was about a month or so into my internship when I changed my mind, and decided to pursue a PhD instead of a job offer. A major factor was the course in epidemiology I’d taken earlier that year. I’d become fascinated by the idea that disease outbreaks didn’t have to be this mysterious, unpredictable occurrence. With the right methods, we could pick them apart,
