research; by the time the infections end, fundamental questions
about contagion can remain unanswered. That’s why building long-
term research capacity is essential. Although our research team has
managed to generate a lot of data on the Zika outbreak in Fiji, we
were only able to do this because we already happened to be there
investigating dengue. Similarly, some of the best data on Zika have
come from a long-running Nicaraguan dengue study led by Eva
Harris at the University of California, Berkeley.[21]
Researchers have also lagged behind outbreaks in other fields.
Many studies of misinformation during the 2016 US election weren’t
published until 2018 or 2019. Other research projects looking at
election interference have struggled to get off the ground at all, while
some are now impossible because social media companies –
whether inadvertently or deliberately – have deleted the necessary
data.[22] At the same time, fragmented and unreliable data sources
are hindering research into banking crises, gun violence and opioid
use.[23]
Getting data is only part of the problem, though. Even the best
outbreak data will have quirks and caveats, which can hinder
analysis. In her work tracking radiation and cancer, Alice Stewart
noted that epidemiologists rarely have the luxury of a perfect
dataset. ‘You’re not looking for a spot of trouble against a spotless
backdrop,’ she said,[24] ‘you’re looking for a spot of trouble in a very
messy situation.’ The same issue crops up in many fields, whether
trying to estimate the spread of obesity in friendship data, uncover
patterns of drug use in the opioid epidemic, or trace the effects of
information across different social media platforms. Our lives are
messy and complicated, and so are the datasets they produce.
If we want a better grasp of contagion, we need to account for its
dynamic nature. That means tailoring our studies to different
outbreaks, moving quickly to ensure our results are as useful as
possible, and finding new ways to thread strands of information
together. For example, disease researchers are now combining data
on cases, human behaviour, population immunity, and pathogen
evolution to investigate elusive outbreaks. Taken individually, each