Social Network Sensors for Early Detection of Contagious Outbreaks

By Nicholas A. Christakis, James H. Fowler. PLoS ONE, 2010

545 citations recorded by the source corpus.

Current methods for the detection of contagious outbreaks give contemporaneous information about the course of an epidemic at best. It is known that individuals near the center of a social network are likely to be infected sooner during the course of an outbreak, on average, than those at the periphery. Unfortunately, mapping a whole network to identify central individuals who might be monitored for infection is typically very difficult. We propose an alternative strategy that does not require ascertainment of global network structure, namely, simply monitoring the friends of randomly selected individuals. Such individuals are known to be more central. To evaluate whether such a friend group could indeed provide early detection, we studied a flu outbreak at Harvard College in late 2009. We followed 744 students who were either members of a group of randomly chosen individuals or a group of their friends. Based on clinical diagnoses, the progression of the epidemic in the friend group occurred 13.9 days (95% C.I. 9.9–16.6) in advance of the randomly chosen group (i.e., the population as a whole). The friend group also showed a significant lead time (p<0.05) on day 16 of the epidemic, a full 46 days before the peak in daily incidence in the population as a whole. This sensor method could provide significant additional time to react to epidemics in small or large populations under surveillance. The amount of lead time will depend on features of the outbreak and the network at hand. The method could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks.

Subfields: Epidemiology, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/65cfac267bb16a95b98988a7a38381df71f3f659

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Citations in the corpus

The "Majority Illusion" in Social Networks (2015, unknown): “Despite its almost nonsensical nature, the friendship paradox has been used to design efficient strategies for vaccination [12], social intervention [22], and early detection of contagious outbreaks [11, 16].”
The Friendship Paradox and Systematic Biases in Perceptions and Social Norms (2016, background): “In particular, agents do not know how many friends each of their friends has (or will have).6 6Indeed, there is evidence that people know little about many friends their friends have (e.g., see Friedkin (1983); Krackhardt (1987, 2014)).”
Systems Science Methods in Public Health (2012, unknown): “Christakis NA, Fowler JH. 2007.”
Social contagion theory: examining dynamic social networks and human behavior (2011, methodology): “Using similar modeling approaches and exploiting data from many sources, we have examined the “spread” of obesity [14, 15], smoking [16], alcohol consumption [17], health screening [18], happiness [19], loneliness [20], depression [21], sleep [22], drug use [22], divorce [23], food consumption [24],”
The Economic Consequences of Social Network Structure (2017, unknown): “Theorems bounding distances by such methods have been proven for increasingly rich network models, starting from simple ones (Erdős and Rényi (1959)), to ones with richer degree distributions (Chung and Lu (2002)), to ones rewired from networks with high clustering (Watts and Strogatz (1998)), to on”
REACT (2020, methodology): “To monitor the community, we will use the social network sensors approach [8].”
Can Network Theory-Based Targeting Increase Technology Adoption? (2018, unknown): “47 This “friends of friends” approach to identifying central people was inspired by Kim et al (2015), Feld (1991) and Christakis and Fowler (2010), who note that randomly selected connections tend to be more central than randomly selected nodes in a network.”
Mathematical modeling of infectious disease dynamics (2013, background): “…network from a diary-based survey from 49 adults who recorded 8661 encounters with 3528 different individuals over 14 non-consecutive days.123 Christakis and Fowler (2010) studied a flu outbreak at Harvard University in 2009.129 Following 744 students they mapped the transmission network…”
Control Centrality and Hierarchical Structure in Complex Networks (2012, background): “networks, a similar strategy has been proposed for efficient immunization [45] and the early detection of contagious outbreaks [46], relying on the statistical trend that randomly selected”