Social Network Sensors for Early Detection of Contagious Outbreaks

Social Network Sensors for Early Detection of Contagious OutbreaksSummaryIntroductionCentral Nodes versus Random PopulationUsing Friends as Proxy for Central NodesEvaluating the Effectiveness of this MethodImportant ClarificationResultsFriend Sample versus Random Sample Early PredictionNetwork StructureCouple of Discussion Points




Central Nodes versus Random Population


Using Friends as Proxy for Central Nodes

"This strategy exploits an interesting property of human social networks: on average, the friends of randomly selected people possess more links (have higher degree) and are also more central (e.g., as measured by betweenness centrality) to the network than the initial, randomly selected people who named them. Therefore, we expect a set of nominated friends to get infected earlier than a set of randomly chosen individuals (who represent the population as a whole). More specifically, a random sample of individuals from a social network will have a mean degree of (the mean degree for the population); but the friends of these random individuals will have a mean degree of plus a quantity defined by the variance of the degree distribution divided by . Hence, when there is variance in degree in a population, and especially when there is high variance, the mean number of contacts for the friends will be greater (and potentially much greater) than the mean for the random sample."


Evaluating the Effectiveness of this Method


Randomly SelectedNominated by the Randomly Selected (Friends)Total (Including Friends of Friends)

In addition, as a byproduct of empaneling the foregoing group of 744 students, we wound up having information about a total of 1,789 uniquely identified Harvard College students (who either participated in the study or who were nominated as friends or as friends of friends); we used this information to draw the social network of part of the Harvard College student body

Tracked cases of influenza via:

  1. Formal diagnosis as well as
  2. Utilizing University Health Services data on the sample

"…we also collected self-reported flu symptom information from participants via email twice weekly (on Mondays and Thursdays), continuing until December 31, 2009. The students were queried about whether they had had a fever or flu symptoms since the last email contact, and there was very little missing data (47% of the subjects completed all of the biweekly surveys, and 90% missed no more than two of the surveys).

Self-report of symptoms rather than serological testing is the current standard for flu diagnosis. Similar to previous studies, students were deemed to have a case of flu (whether seasonal or the H1N1 variety) if they reported having a fever of greater than 100 F (37.8 C) and at least two of the following symptoms: sore throat; cough; stuffy or runny nose; body aches; headache; chills; or fatigue. We checked the sensitivity of our findings by using definitions of flu that required more symptoms, and our results did not change."

Important Clarification

"To be clear, we are not suggesting that a person’s precise position in the observed network, nor indeed whether he was nominated as a friend or not (and by whom), traces out the actual path by which he acquired (or did not acquire) the flu. The topological parameters we measured here, or indeed the fact that a person was deemed to be a member of the friend group, serve as proxies for the subject’s actual location within what is an essentially unobservable social network (including real friends, relatives, casual contacts, and so on) through which the flu spreads by interpersonal means. Being a ‘‘friend’’ is a marker for a person’s social network position, whatever the path of infection to this person actually is. Of course, it is likely that measured friendship networks are related to contact networks more generally: for instance, people with more friends should come into greater contact with more strangers both directly and indirectly via their friends."



In the case of both the clinical and self-reported diagnostic standards, the estimates are robust to a number of control variables including H1N1 vaccination, seasonal flu vaccination, sex, college class, and inter-collegiate sports participation

Friend Sample versus Random Sample


Early Prediction

Thus, a comparison of outcomes in the friends group and the randomly chosen group could be an effective technique for detecting outbreaks at early stages of an epidemic.

They also wondered if you could simply ask people to describe their own popularity in order to get the same predictive information. They found that this method did not work as well.

These results suggest that being nominated as a friend captures more network information (including the tendency to be central in the network) than self-reported network attributes.


Network Structure

With their data, they were able to create a small network and calculate certain features about that network such as:

They point out that this is only possible because they are sampling from a small network (Harvard) and this would likely not work if you were to do this sampling from a much larger population like a city.


The results showed that, as expected, the friend group differed significantly from the random group for all these measures

Each of these are potentially helpful measures for early prediction of spreading phenomena.

They go into a bit more detail testing how each of these parameters helps for prediction in a bit more detail within the original text but I am leaving that out because it's not very important.


Couple of Discussion Points

"The ability of the proposed method to detect outbreaks early, and how early it might do so, will depend on intrinsic properties of the thing that is spreading…"



Notes by Matthew R. DeVerna