Research article Special Issues

Relapse-driven bistability in a nonlinear model of gang dynamics with deterrence and reformed-led advocacy

  • Published: 16 September 2026
  • A central question in gang-intervention modeling is whether reducing recruitment below an invasion threshold is enough to eliminate gang activity. Here, we show that relapse from a reformed class can make it insufficient. We developed a deterministic compartmental SGR model with susceptible, active gang, and reformed classes, in which recruitment was reduced by perceived-risk deterrence and by an advocacy interaction between reformed and susceptible individuals. We established positive invariance and an invasion threshold for a weighted-contact formulation, and showed that its reciprocity-consistent proportional-mixing reduction exhibits backward bifurcation when relapse is sufficiently strong. The backward bifurcation generates a subthreshold positive gang equilibrium even when the simplified reproduction number is below one; for the worked parameter regime, this branch turns at a saddle-node and yields bistability, so the long-term outcome is set by basin membership rather than by the threshold alone. In dimensionless form, the backward-bifurcation condition reduces, under typical parameter regimes, to the requirement that the dimensionless relapse rate exceed the natural outflow rate of the active gang class. A worked instance at $\mathcal R_0^{\rm{simp}} = 0.5$ yields gang-free and high-gang attractors separated by a low-prevalence saddle with a substantial reformed-class reservoir; numerical continuation places the saddle-node at $\mathcal R_0^{\rm{simp}}\approx 0.08755$, so the bistable interval spans approximately the upper 91% of the unit interval. Numerical bifurcation diagrams and a baseline-centered PRCC sensitivity analysis identify relapse and reformation as the dominant opposing drivers of long-term gang prevalence in the sampled regime, with recruitment a smaller positive driver. These results suggest that gang reduction may require coordinated strategies that reduce recruitment, prevent relapse, strengthen exit pathways, and sustain reformed-led prevention efforts.

    Citation: Theophilus Kwofie, Lucero Rodriguez Rodriguez, Xia Wang, James M. Hyman, Yun Kang. Relapse-driven bistability in a nonlinear model of gang dynamics with deterrence and reformed-led advocacy[J]. Mathematical Biosciences and Engineering, 2026, 23(9): 2807-2859. doi: 10.3934/mbe.2026100

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  • A central question in gang-intervention modeling is whether reducing recruitment below an invasion threshold is enough to eliminate gang activity. Here, we show that relapse from a reformed class can make it insufficient. We developed a deterministic compartmental SGR model with susceptible, active gang, and reformed classes, in which recruitment was reduced by perceived-risk deterrence and by an advocacy interaction between reformed and susceptible individuals. We established positive invariance and an invasion threshold for a weighted-contact formulation, and showed that its reciprocity-consistent proportional-mixing reduction exhibits backward bifurcation when relapse is sufficiently strong. The backward bifurcation generates a subthreshold positive gang equilibrium even when the simplified reproduction number is below one; for the worked parameter regime, this branch turns at a saddle-node and yields bistability, so the long-term outcome is set by basin membership rather than by the threshold alone. In dimensionless form, the backward-bifurcation condition reduces, under typical parameter regimes, to the requirement that the dimensionless relapse rate exceed the natural outflow rate of the active gang class. A worked instance at $\mathcal R_0^{\rm{simp}} = 0.5$ yields gang-free and high-gang attractors separated by a low-prevalence saddle with a substantial reformed-class reservoir; numerical continuation places the saddle-node at $\mathcal R_0^{\rm{simp}}\approx 0.08755$, so the bistable interval spans approximately the upper 91% of the unit interval. Numerical bifurcation diagrams and a baseline-centered PRCC sensitivity analysis identify relapse and reformation as the dominant opposing drivers of long-term gang prevalence in the sampled regime, with recruitment a smaller positive driver. These results suggest that gang reduction may require coordinated strategies that reduce recruitment, prevent relapse, strengthen exit pathways, and sustain reformed-led prevention efforts.



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    [1] J. M. Hagedorn, The global impact of gangs, J. Contemp. Crim. Justice, 21 (2005), 153–169. https://doi.org/10.1177/1043986204273390 doi: 10.1177/1043986204273390
    [2] H. C. Covey, Street Gangs Throughout the World, Charles C Thomas, Springfield, IL, 2010.
    [3] M. W. Klein, F. M. Weerman, T. P. Thornberry, Street gang violence in Europe, Eur. J. Criminol., 3 (2006), 413–437. https://doi.org/10.1177/1477370806067911 doi: 10.1177/1477370806067911
    [4] M. W. Klein, H. J. Kerner, C. L. Maxson, E. G. M. Weitekamp, The Eurogang Paradox: Street Gangs and Youth Groups in the U.S. and Europe, Kluwer Academic Publishers, Dordrecht, 2001. https://doi.org/10.1007/978-94-010-0882-2
    [5] National Gang Center, National Youth Gang Survey Analysis, 2023. Available from: https://nationalgangcenter.ojp.gov/survey-analysis.
    [6] National Gang Intelligence Center, 2011 National Gang Threat Assessment: Emerging Trends, 2012. Available from: https://www.fbi.gov/stats-services/publications/2011-national-gang-threat-assessment.
    [7] F. M. Thrasher, The Gang: A Study of 1,313 Gangs in Chicago, University of Chicago Press, Chicago, IL, 1927.
    [8] S. H. Decker, B. Van Winkle, Life in the Gang: Family, Friends, and Violence, Cambridge University Press, Cambridge, 1996.
    [9] A. K. Cohen, Delinquent Boys: The Culture of the Gang, Free Press, New York, 1955.
    [10] R. A. Cloward, L. E. Ohlin, Delinquency and Opportunity: A Study of Delinquent Gangs, Free Press, New York, 1960.
    [11] W. F. Whyte, Street Corner Society: The Social Structure of an Italian Slum, University of Chicago Press, Chicago, IL, 1943.
    [12] E. Anderson, Code of the Street: Decency, Violence, and the Moral Life of the Inner City, W. W. Norton, New York, 2000.
    [13] R. Chapman, Culture Wars: An Encyclopedia of Issues, Viewpoints and Voices, Routledge, New York, 2015.
    [14] S. H. Decker, Collective and normative features of gang violence, Justice Q., 13 (1996), 243–264. https://doi.org/10.1080/07418829600092931 doi: 10.1080/07418829600092931
    [15] D. C. Pyrooz, "From your first cigarette to your last dyin' day": The patterning of gang membership in the life-course, J. Quant. Criminol., 30 (2014), 349–372. https://doi.org/10.1007/s10940-013-9206-1 doi: 10.1007/s10940-013-9206-1
    [16] T. P. Thornberry, M. D. Krohn, A. J. Lizotte, C. A. Smith, K. Tobin, Gangs and Delinquency in Developmental Perspective, Cambridge University Press, Cambridge, 2003.
    [17] F. A. Esbensen, L. T. Winfree Jr., N. He, T. J. Taylor, Youth gangs and definitional issues: When is a gang a gang, and why does it matter, Crime Delinq., 47 (2001), 105–130. https://doi.org/10.1177/0011128701047001005 doi: 10.1177/0011128701047001005
    [18] K. G. Hill, J. C. Howell, J. D. Hawkins, S. R. Battin-Pearson, Childhood risk factors for adolescent gang membership: Results from the Seattle Social Development Project, J. Res. Crime Delinq., 36 (1999), 300–322. https://doi.org/10.1177/0022427899036003003 doi: 10.1177/0022427899036003003
    [19] F. A. Esbensen, D. Peterson, T. J. Taylor, A. Freng, Similarities and differences in risk factors for violent offending and gang membership, Aust. N. Z. J. Criminol., 42 (2009), 310–335. https://doi.org/10.1375/acri.42.3.310 doi: 10.1375/acri.42.3.310
    [20] M. Bouchard, A. Spindler, Groups, gangs, and delinquency: Does organization matter, J. Crim. Justice, 38 (2010), 921–933. https://doi.org/10.1016/j.jcrimjus.2010.06.009 doi: 10.1016/j.jcrimjus.2010.06.009
    [21] T. P. Thornberry, M. D. Krohn, A. J. Lizotte, D. Chard-Wierschem, The role of juvenile gangs in facilitating delinquent behavior, J. Res. Crime Delinq., 30 (1993), 55–87. https://doi.org/10.1177/0022427893030001005 doi: 10.1177/0022427893030001005
    [22] D. C. Pyrooz, S. H. Decker, V. J. Webb, The ties that bind: Desistance from gangs, Crime Delinq., 60 (2014), 491–516. https://doi.org/10.1177/0011128710372191 doi: 10.1177/0011128710372191
    [23] National Research Council, Contagion of Violence: Workshop Summary, National Academies Press, Washington, DC, 2013.
    [24] J. B. Bingenheimer, R. T. Brennan, F. J. Earls, Firearm violence exposure and serious violent behavior, Science, 308 (2005), 1323–1326. https://doi.org/10.1126/science.1110096 doi: 10.1126/science.1110096
    [25] C. M. Katz, A. M. Fox, Risk and protective factors associated with gang-involved youth in Trinidad and Tobago, Rev. Panam. Salud Publica, 27 (2010), 187–202. https://doi.org/10.1590/S1020-49892010000300006 doi: 10.1590/S1020-49892010000300006
    [26] P. J. Brantingham, G. E. Tita, M. B. Short, S. E. Reid, The ecology of gang territorial boundaries, Criminology, 50 (2012), 851–885. https://doi.org/10.1111/j.1745-9125.2012.00281.x doi: 10.1111/j.1745-9125.2012.00281.x
    [27] M. B. Short, P. J. Brantingham, A. L. Bertozzi, G. E. Tita, Dissipation and displacement of hotspots in reaction-diffusion models of crime, Proc. Natl. Acad. Sci. U.S.A., 107 (2010), 3961–3965. https://doi.org/10.1073/pnas.0910921107 doi: 10.1073/pnas.0910921107
    [28] G. González-Parra, B. Chen-Charpentier, H. V. Kojouharov, Mathematical modeling of crime as a social epidemic, J. Interdiscip. Math., 21 (2018), 623–643. https://doi.org/10.1080/09720502.2015.1132574 doi: 10.1080/09720502.2015.1132574
    [29] J. Sooknanan, B. Bhatt, D. M. G. Comissiong, Catching a gang: A mathematical model of the spread of gangs in a population treated as an infectious disease, Int. J. Pure Appl. Math., 83 (2013), 25–43. https://doi.org/10.12732/ijpam.v83i1.4 doi: 10.12732/ijpam.v83i1.4
    [30] J. Sooknanan, B. Bhatt, D. M. G. Comissiong, A modified predator–prey model for the interaction of police and gangs, R. Soc. Open Sci., 3 (2016), 160083. https://doi.org/10.1098/rsos.160083 doi: 10.1098/rsos.160083
    [31] R. A. Hegemann, L. M. Smith, A. B. T. Barbaro, A. L. Bertozzi, S. E. Reid, G. E. Tita, Geographical influences of an emerging network of gang rivalries, Physica A, 390 (2011), 3894–3914. https://doi.org/10.1016/j.physa.2011.05.040 doi: 10.1016/j.physa.2011.05.040
    [32] O. F. Anorue, D. E. Atuma, A mathematical model of gang membership and control, Commun. Phys. Sci., 9 (2023), 12–24.
    [33] O. M. Ibrahim, D. Okuonghae, M. N. O. Ikhile, Optimal control model for criminal gang population in a limited-resource setting, Int. J. Dyn. Control, 11 (2023), 835–850. https://doi.org/10.1007/s40435-022-00992-8 doi: 10.1007/s40435-022-00992-8
    [34] T. Kwofie, M. Dogbatsey, S. E. Moore, Curtailing crime dynamics: A mathematical approach, Front. Appl. Math. Stat., 8 (2023), 1086745. https://doi.org/10.3389/fams.2022.1086745 doi: 10.3389/fams.2022.1086745
    [35] J. Sooknanan, B. Bhatt, D. M. G. Comissiong, Life and death in a gang: A mathematical model of gang membership, J. Math. Res., 4 (2012), 10–27. https://doi.org/10.5539/jmr.v4n4p10 doi: 10.5539/jmr.v4n4p10
    [36] X. Wang, L. Zanette, X. Zou, Modelling the fear effect in predator–prey interactions, J. Math. Biol., 73 (2016), 1179–1204. https://doi.org/10.1007/s00285-016-0989-1 doi: 10.1007/s00285-016-0989-1
    [37] F. Nyabadza, C. P. Ogbogbo, J. Mushanyu, Modelling the role of correctional services on gangs: Insights through a mathematical model, R. Soc. Open Sci., 4 (2017), 170511. https://doi.org/10.1098/rsos.170511 doi: 10.1098/rsos.170511
    [38] C. G. Roman, S. H. Decker, D. C. Pyrooz, Leveraging the pushes and pulls of gang disengagement to improve gang intervention: Findings from three multi-site studies and a review of relevant gang programs, J. Crime Justice, 40 (2017), 316–336. https://doi.org/10.1080/0735648X.2017.1345096 doi: 10.1080/0735648X.2017.1345096
    [39] J. A. Butts, C. G. Roman, L. Bostwick, J. R. Porter, Cure violence: A public health model to reduce gun violence, Annu. Rev. Public Health, 36 (2015), 39–53. https://doi.org/10.1146/annurev-publhealth-031914-122509 doi: 10.1146/annurev-publhealth-031914-122509
    [40] D. C. Pyrooz, K. J. Clark, J. J. Tostlebe, S. H. Decker, E. Orrick, Gang affiliation and prisoner reentry: Discrete-time variation in recidivism by current, former, and non-gang status, J. Res. Crime Delinq., 58 (2021), 192–234. https://doi.org/10.1177/0022427820949895 doi: 10.1177/0022427820949895
    [41] S. Busenberg, C. Castillo-Chavez, A general solution of the problem of mixing of subpopulations and its application to risk- and age-structured epidemic models for the spread of AIDS, IMA J. Math. Appl. Med. Biol., 8 (1991), 1–29. https://doi.org/10.1093/imammb/8.1.1 doi: 10.1093/imammb/8.1.1
    [42] P. Hartman, Ordinary Differential Equations, 2$^{nd}$ edition, SIAM, Philadelphia, PA, 2002.
    [43] S. H. Strogatz, Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering, 2$^{nd}$ edition, Westview Press, Boulder, CO, 2014.
    [44] F. A. Esbensen, Youth Violence: Sex and Race Differences in Offending, Victimization, and Gang Membership, Temple University Press, Philadelphia, PA, 2010.
    [45] P. van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6 doi: 10.1016/S0025-5564(02)00108-6
    [46] C. Castillo-Chavez, B. Song, Dynamical models of tuberculosis and their applications, Math. Biosci. Eng., 1 (2004), 361–404. https://doi.org/10.3934/mbe.2004.1.361 doi: 10.3934/mbe.2004.1.361
    [47] S. Blower, H. Dowlatabadi, Sensitivity and uncertainty analysis of complex models of disease transmission: An HIV model, as an example, Int. Stat. Rev., 62 (1994), 229–243. https://doi.org/10.2307/1403510 doi: 10.2307/1403510
    [48] S. Marino, I. B. Hogue, C. J. Ray, D. E. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology, J. Theor. Biol., 254 (2008), 178–196. https://doi.org/10.1016/j.jtbi.2008.04.011 doi: 10.1016/j.jtbi.2008.04.011
    [49] A. B. Gumel, E. A. Iboi, C. N. Ngonghala, E. H. Elbasha, A primer on using mathematics to understand COVID-19 dynamics: Modeling, analysis and simulations, Infect. Dis. Model., 6 (2021), 148–168. https://doi.org/10.1016/j.idm.2020.11.005 doi: 10.1016/j.idm.2020.11.005
    [50] F. A. Esbensen, D. Peterson, T. J. Taylor, D. W. Osgood, Results from a multi-site evaluation of the G.R.E.A.T. program, Justice Q., 29 (2012), 125–151. https://doi.org/10.1080/07418825.2011.585995 doi: 10.1080/07418825.2011.585995
    [51] V. Hutson, A theorem on average Liapunov functions, Monatsh. Math., 98 (1984), 267–275. https://doi.org/10.1007/BF01540776 doi: 10.1007/BF01540776
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