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WE’RE zeroing in. In a disease outbreak, “patient zero” is the first person to be infected, and finding them can help stop the outbreak. But incomplete data mean that person is usually hard to track down. Now an algorithm could help with that hunt.

Mile Šikic of the University of Zagreb, Croatia, and his colleagues focused on a scenario in which there is a network of infected and uninfected people, but you don’t know when or who the infection passed between. This could be sexually transmitted infections (STIs), the spread of information on a social network, or computer viruses that lay dormant before activating.

The team’s algorithm simulates potential spreads through the network and compares them with real data, to calculate the probability that a particular person is patient zero. If just one person has a probability of 100 per cent, you have found the origin. But if multiple people have a score then you need more data to find patient zero (Physical Review Letters, doi.org/5h4).


It turns out that the origin is easier to find if the infection spreads quickly. “With a slower process, you lose some kind of information,” Šikic says.

The team tested the method on STI data from a Brazilian website in which people anonymously review encounters with sex workers. They found that 60 per cent of the time the algorithm either correctly identified patient zero, or was one hop away. “If we cannot say who is patient zero, we can be in their neighbourhood,” says Šikic.

This article appeared in print under the headline “How to home in on patient zero during disease outbreaks”