Flu Seasonally Adjusted

Permutations points out an elegant paper from Christakis and Fowler (gloriously open access). They exploit a clever result from social network theory called the friends paradox. This is the phenomenon that your friends have more friends than you do – because social networks typically have a few very connected spoke nodes. They use this to track flu within a university student population. By separately tracking the friend cohort they were able to note the evolution of a flu epidemic several weeks before its full arrival in the general population as represented by the random cohort.

Current surveillance methods for the flu, such as those implemented by the CDC that require collection of data from subjects seeking outpatient care or having lab tests, are typically lagging indicators about the timing of the epidemic (information is typically one to two weeks behind the actual course of the epidemic). […] [W]hile potentially instantaneous, the Google Trends and prediction market methods would only, at best, give contemporaneous information about rates of infection. In contrast, we show that the sensor method described here can detect an outbreak of flu two weeks in advance. That is, the sensor network method provides early detection rather than just rapid warning.

Wiring up a distributed computer of neighbourhood gossips to see into the future is presumably a trick with wider applications. For instance, economic data is not only notoriously bad, but notoriously slow. It’s a field where price data from three month old lagging indicators are siezed on with delight at their timeliness, and GDP figures have to be seasonally adjusted a year after the period they apply to. Economic actors also behave as a network for the flow of information and beer.

So, you should be able to systematically exploit this effect in economic surveys to get both more timely results, and information on the velocity of effects throughout an economy. Eg, if you are surveying businesses, get those businesses to also nominate their suppliers and customers, and track that group as well. It’s possible this technique is already used, and I’d be interested to hear about it. I suspect that its main use is though data collection folk wisdom rather than systematically. So it’s well known that health workers are vulnerable, highly connected nodes for disease spread / containment, and that banks and large retailers are hubs of economic activity, but that knowledge is not generalisable in the same way as the friend cohort in the paper. Perhaps you could even use techniques like this to build a network model of critical financial institutions, from the perspective of vulnerability to systemic failure under a catastrophic crisis.