When Randy Shilts came across the CDC diagram while researching his book, his attention was drawn to Dugas. ‘In the middle of that study was a circle with an O next to it, and I always thought it was Patient O,’ he later recalled. ‘When I went to the CDC, they started talking about Patient Zero. I thought, “Ooh, that’s catchy”.’[67]
It’s easier to tell a story when it has a clear antagonist. According to historian Phil Tiemeyer, it was Shilts’s editor Michael Denneny who suggested they make Dugas the villain in the book and accompanying publicity. ‘Randy hated the idea,’ Denneny told Tiemeyer. ‘It took me almost a week to argue him into it.’ The decision – which Denneny later said he regretted – came because the media seemed to have little interest in aids otherwise. ‘They were not going to review a book that was an indictment of the Reagan administration and the medical establishment.’[68]
When discussing outbreaks that involve superspreading events, there is a tendency to place all attention on the people apparently at the centre of them. Who are these ‘superspreaders’? What makes them different from everyone else? However, such attention can be misplaced. Take that story of the Belgrade teacher who arrived in hospital with smallpox. There was nothing intrinsically unusual about him or his behaviour. He had acquired the disease through a chance encounter, had tried to get medical care at an appropriate place – a hospital – and the outbreak spread because nobody initially suspected smallpox was the cause. This is true of many outbreaks: it’s often difficult to predict in advance what role a specific individual will play.
Even if we can identify situations that create a risk of disease transmission, it won’t necessarily lead to the outcome we expect. On 21 October 2014, at the height of the Ebola epidemic in West Africa, a two-year-old girl arrived at a hospital in the city of Kayes, Mali. Following the death of her father, who had been a healthcare worker, the girl had travelled over 1,200 km from neighbouring Guinea with her grandmother, uncle and sister. At the Kayes hospital, the girl tested positive for Ebola, and would die of the disease the next day. She was Mali’s first case of Ebola, and health authorities began to search for people who may have come into contact with her. During her trip, she’d taken at least one bus and three taxis, potentially interacting with dozens if not hundreds of people. She’d already been displaying symptoms when she arrived at the hospital; based on the nature of Ebola transmission, there was a good chance she could have passed the virus on. Investigators eventually managed to track down over one hundred of the girl’s contacts and placed them in quarantine as a precaution. However, none of them came down with Ebola. Despite her long journey, the girl hadn’t infected anyone.[69]
When Ebola superspreading events did occur during 2014–15, our team noticed there was one feature that stood out. Unfortunately, it wasn’t a particularly helpful one: the cases most likely to be involved in superspreading were the ones that couldn’t be linked to existing chains of transmission. Put simply, the people driving the epidemic were generally the ones the health authorities didn’t know about. These people went undetected until they sparked a new set of infections, making it near impossible to predict superspreading events.[70]
With enough effort, we can often trace some of the path of infection during an outbreak, reconstructing who might have infected whom. It can be tempting to construct a narrative as well, speculating about why certain people transmitted more than others. However, just because an infection is capable of superspreading doesn’t necessarily mean the same people are always the superspreaders. Two people might behave in almost the same way, but by chance one of them spreads infection and the other does not. When history is written, one is blamed and the other ignored. Philosophers call it ‘moral luck’: the idea that we tend to view actions with unfortunate consequences as worse than equal actions without any repercussions.[71]
Sometimes the people involved in an outbreak do behave differently, but not necessarily in the way we might assume. In his book The Tipping Point, Malcolm Gladwell describes an outbreak of gonorrhea in Colorado Springs, Colorado, during 1981. As part of the outbreak investigation, epidemiologist John Potterat and his colleagues had interviewed 769 cases, asking whom they’d recently had sexual contact with. Of these cases, 168 people had at least two contacts who were also infected. This suggested they were disproportionately important in the outbreak. ‘Who were those 168 people?’ Gladwell asked. ‘They aren’t like you or me. They are people who go out every night, people who have vastly more sexual partners than the norm, people whose lives and behaviour are well outside of the ordinary.’
Were these people really so promiscuous and unusual? Not particularly, in my view: the researchers found that, on average, these cases reported sexual encounters with 2.3 other infected people. This implies they caught the infection from one person and typically gave it to one or two others. Cases tended to be black or Hispanic, young, and associated with the military; almost half had known their sexual partners for more than two months.[72] During the 1970s, Potterat had begun to notice that promiscuity wasn’t a good explanation for gonorrhea outbreaks in Colorado Springs. ‘Especially striking was the difference in gonorrhea test outcome between sexually adventurous white women from a local upper middle class college and similarly aged black women with modest sexual histories and educational backgrounds,’ he noted.[73] ‘The former were seldom diagnosed with gonorrhea, unlike the latter.’ 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
