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When the next highly infectious disease begins to spread through a densely populated metropolitan area, the authorities will have a only short period of time to put in place emergency measures to prevent an epidemic. Hit this window of opportunity and the number of casualties could be relatively small. Miss it and the number could be frighteningly large.

So it’s no surprise that there is significant interest in finding efficient ways to spot the early cases. The stakes could hardly be larger.

Which is why the work of Lijun Sun at the Future Cities Laboratory in Singapore, and a couple of pals, is interesting. These guys say they’ve discovered a small group of special individuals in a city that act as super-spreaders of disease. These people have unusually large numbers of contacts with others and so spread illness far and wide, when they become infected. That’s handy because these individuals should become infected first during any epidemic. So a system to monitor them would act as an important early warning indicator of an incipient epidemic and give authorities a crucial early warning of potential disaster.

The data that Lijun and co use to find these super-spreaders comes from the analysis of contacts between commuters on Singapore’s bus system. Over 7 days these guys monitored the tapping in/tapping out data from smart cards that Singapore’s 3 million commuters use to access the transport system.

This showed whenever individual commuters shared the same bus, how often, for how long and at what time of day. That revealed commuting patterns in unprecedented detail.

This data also turns out to be exactly what epidemiologists need to study the spread of disease through a city. For many disease, the chances of infection depend directly on the length of contact between an infected individual and a susceptible one.  

Until now models of how disease spreads have all relied on simulated networks, largely because of the difficulty in gathering good data on the structure person-person contact networks in real cities.

The Singapore data set changes all the that. For the first time, Lijun and co have been able to simulate the spread of disease through a real network to see what happens.

They began by releasing 10 infected individuals into the city on a Saturday and then watched as the number of infected people rose throughout the week.

That kind of ‘God’s-eye’ view of contagion would be hugely useful in preventing the large-scale spread of the disease but it is entirely impractical to achieve.

Instead, Lijun and co asked whether it is possible to measure the spread of disease by choosing a smaller subset of the population and monitoring them instead. The crucial factor must be that these people are more highly connected than usual since those having more contact with others would be more likely to contract the disease early in an epidemic.

One way to find a group of well connected individuals is to select a group of people at random and then choose another set of people who are all friends of the first set. This set of friends will always be better connected than the random group since people with large numbers of friends are more likely to be selected in this way.

So Lijiun and co selected a group of friends and monitored how quickly they became infected in these simulated city outbreaks. Sure enough, friends tend to become infected earlier and so do act as a kind of early warning system for epidemics.

But interestingly, Lijun and co also identified another group of individuals who are even better connected than the friends group and so act as an even more sensitive sensor of disease. These people are super-commuters who come into contact with far more individuals than most other people during their travels.

This also makes them super-spreaders of disease too. “In detecting and containing contagious outbreaks, it is crucial to identify “super-spreaders”, as they may provide significant lead indicators for the early response of public health agencies,” say Lijun and co.

That makes this group of super-spreaders hugely valuable since they provide the best kind of early warning system at minimal cost.

The big question of course is how to find these people. One possibility is to crowdsource data on disease, to ask people to willingly provide anonymous information about their health status, And if they also provided information about their commuting habits, it might be possible to pick out the super spreaders.

Something like this is already possible. Google already monitors the use of search terms related to disease to track flu. It ought to be a simple step to correlate this with an individual’s travel patterns. But whether this can identify super commuters and spreaders is not clear. 

Nevertheless, Lijun and co have identified a useful new way to track disease. Let’s just hope that it can be brought to bear quickly if ever it becomes necessary to use it.

Ref: http://arxiv.org/abs/1401.2815: Efficient Detection Of Contagious Outbreaks In Massive Metropolitan Encounter Networks

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