Modeling Contagion Through Social Networks to Explain and Predict Gunshot Violence in Chicago, 2006 to 2014

Abstract

IMPORTANCEEvery day in the United States, more than 200 people are murdered or assaulted with a firearm. Little research has considered the role of interpersonal ties in the pathways through which gun violence spreads.OBJECTIVE To evaluate the extent to which the people who will become subjects of gun violence can be predicted by modeling gun violence as an epidemic that is transmitted between individuals through social interactions.DESIGN, SETTING, AND PARTICIPANTS This study was an epidemiological analysis of a social network of individuals who were arrested during an 8-year period in Chicago, Illinois, with connections between people who were arrested together for the same offense. Modeling of the spread of gunshot violence over the network was assessed using a probabilistic contagion model that assumed individuals were subject to risks associated with being arrested together, in addition to demographic factors, such as age, sex, and neighborhood residence. Participants represented a network of 138 163 individuals who were arrested between January 1, 2006, and March 31, 2014 (29.9% of all individuals arrested in Chicago during this period), 9773 of whom were subjects of gun violence. Individuals were on average 27 years old at the midpoint of the study, predominantly male (82.0%) and black (75.6%), and often members of a gang (26.2%). MAIN OUTCOMES AND MEASURESExplanation and prediction of becoming a subject of gun violence (fatal or nonfatal) using epidemic models based on person-to-person transmission through a social network.RESULTS Social contagion accounted for 63.1% of the 11 123 gunshot violence episodes; subjects of gun violence were shot on average 125 days after their infector (the person most responsible for exposing the subject to gunshot violence). Some subjects of gun violence were shot more than once. Models based on both social contagion and demographics performed best; when determining the 1.0% of people (n = 1382) considered at highest risk to be shot each day, the combined model identified 728 subjects of gun violence (6.5%) compared with 475 subjects of gun violence (4.3%) for the demographics model (53.3% increase) and 589 subjects of gun violence (5.3%) for the social contagion model (23.6% increase).CONCLUSIONS AND RELEVANCE Gunshot violence follows an epidemic-like process of social contagion that is transmitted through networks of people by social interactions. Violence prevention efforts that account for social contagion, in addition to demographics, have the potential to prevent more shootings than efforts that focus on only demographics.

Neighborhood Interventions to Reduce Violence

“…Violence is clearly a health problem because victims experience physical injuries, premature death, and adverse health behaviors (7,19,73,79). Violence is also clustered in time and space (13,40). Exposure to violence may lead to the intergenerational transmission of violence, or a "cycle of violence" (87), where childhood experiences such as abuse and neglect can lead to later victimization, perpetration (2), or self-harm (30,68).…”

Violent reinjury risk assessment instrument (VRRAI) for hospital-based violence intervention programs

“…52 Physical and social proximity to higher risk individuals—especially those involved in gun violence—has been shown to increase rates of co-offending (being arrested for the same offense), amplifying risky behaviors, and being more predictive of victimization than demographic information alone. 6,53–55 For these reasons, two essential threads to be assessed are level of participation in gangs and in illegal activities, and incarceration history. In consideration of this fact, these factors’ placement in Category A (elevated-risk indicators) is justified.…”

“…1 Many of these patients suffer a subsequent violent injury, with recidivism rates as high as 58%. 3–6 Hospital-based violence intervention programs (HVIP) have been created to reduce injury recidivism among patients at risk of re-injury. However, to our knowledge no formal methods of assessing patient’s risk of re-injury have been developed.…”

Using Social Media and Social Network Analysis in Law Enforcement: Creating a Research Agenda, Including Business Cases, Protections, and Technology Needs

“…As noted, high-risk individuals tend to be central in the social networks of those involved in crime (e.g., Green, Horel, and Papachristos, 2017;Papachristos, Wildeman, and Roberto, 2015). Here, being central typically relates to "degree centrality"-having comparatively large numbers of links to others involved in criminal activity, either directly or indirectly (for subjects who link to others who, in turn, are linked to many others).…”

“…Research has shown that subjects who have numbers of co-arrest links that make them part of social networks with members suffering lots of violent attacks are likely to be parties of violence themselves (Green, Horel, and Papachristos, 2017;Papachristos, Wildeman, and Roberto, 2015).…”