follow this rule: the vast majority of people had slept with at most one
person in the past year, whereas some reported dozens of partners.
Researchers have since found similar patterns of sexual behaviour in
countries ranging from Burkina Faso to the United Kingdom.[59]
What effect does this extreme variability in number of partners
have on outbreaks? In the 1970s, mathematician James Yorke and
his colleagues noticed there was a problem with the ongoing
gonorrhea epidemic in the United States. Namely, it didn’t seem
possible. For the disease to keep spreading, the reproduction number
needed to be above one. That meant infected people should on
average have at least two recent sexual partners: one who gave the
infection to them, and another who they passed it on to. But a study
of patients with gonorrhea had found that they’d had only 1.5 recent
partners on average.[60] Even if the probability of transmission during
sex was very high, it suggested that there simply weren’t enough
encounters for the disease to persist. What was going on?
If we just take the average number of partners, we are ignoring the
fact that not everyone’s sex lives are the same. This variability is
important: if someone has a lot of partners, we’d expect them to be
both more likely to get infected and more likely to pass the infection
on. We therefore need to account for the fact that they can contribute
to transmission in these two different ways. Yorke and his colleagues
argued that this might explain why there could be a gonorrhea
epidemic, despite people having few partnerships on average: people
with lots of contacts might be contributing disproportionality to the
spread, pushing the reproduction number above one. Anderson and
May would later show that the more variation there was in the number
of partners people had, the higher we’d expect the reproduction
number to be.
Identifying people who are at higher risk – and finding ways to
reduce this risk – can help stop an outbreak in its early stages. In the
late 1980s, Anderson and May suggested that STIs would initially
spread quickly through such high-risk groups, even though the overall
outbreak would be smaller than we’d expect if everyone mixed at
random.[61]