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Spread of Crime Dynamics: A Mathematical Approach

Year 2024, Volume: 16 Issue: 2, 481 - 489, 31.12.2024
https://doi.org/10.47000/tjmcs.1555972

Abstract

In this work, the spread of crime dynamics in the US is analyzed from a mathematical perspective. An epidemiological model is established, including five compartments: Susceptible ($S$), Latent 1 ($E_1$), Latent 2 ($E_2)$, Incarcerated ($I$), and Recovered ($R$). A system of differential equations is used to model the spread of crime. A result demonstrating the positivity of the solutions for the system is included. The basic reproduction number and the stability of the disease-free equilibrium are calculated following epidemiological theories. Numerical simulations are performed with US-specific parameter values. Understanding the dynamics of the spread of crime helps to determine what factors may work best to reduce violent crime effectively.

Ethical Statement

In submitting this manuscript, we affirm that the research presented adheres to the highest ethical standards. We confirm that: This work is original and has not been published elsewhere nor is it under consideration by any other publication. All authors listed on the manuscript have significantly contributed to the research and writing process. We acknowledge that each author is accountable for the content of the work. We disclose any potential conflicts of interest that may influence our research or its interpretation. No such conflicts exist. We have properly cited all sources and provided appropriate credit to others’ work. By submitting this manuscript, we commit to uphold these ethical principles and ensure the integrity of the scientific community. Sincerely, Dr. Kubilay Dagtoros Dr. Ana Vivas Dr. Sujan Pant Mr. Michael Aguadze September 25, 2024

Supporting Institution

Norfolk State University

Thanks

We thank to the editorial board for reviewing our manuscript.

References

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  • Akers, R.L., Sellers, C.S., Criminological Theories: Introduction, Evaluation, and Application, Oxford University Press, 2012.
  • Alper, M., Durosse, M.R., Markman, J., Update on prisoner recidivism, NCJ 250975, (2018).
  • Banks, H.T., Castillo-Chavez, C., Bioterrorism: Mathematical modeling applications in homeland security, Society for Industrial and Applied Mathematics, Philadelphia, PA, 2003.
  • Becker, G., Crime and punishment: An economic approach, J. Politic. Econ. 76(1968), 169–217.
  • Brauer, F., Driessche, P.V.D., Models for transmission of disease with immigration of infectives, Math. Biosci., 171(1995), 143–154.
  • Carson, E.A., Mortality in state and federal prisons 2001-2019, U.S. Department of Justice, Office of Justice Programs Bureau of Justice Statistics, (2021).
  • Castillo-Chavez, C., Feng, Z., Huang, W., On the computation of R0 and its role on global stability, Mathematical Approaches for Emerging and Reemerging Infectious Diseases: An Introduction, Vol. 125 of IMA, Springer, New York, (2002), 229–250.
  • Clinard, M.B., Meier, R.F., Sociology of Deviant Behavior, Wadsworth, Belmont, 2010.
  • Crane, J., The epidemic theory of ghettos and neighborhood effects on dropping out and teenage childbearing, Amer. J. Sociol., 96(1991), 1226–1259.
  • Diekmann, O., Heesterbeek, J.A.P., Metz, J.A.J., On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations, J. Math. Biol., 28(4)(1990), 365–382.
  • Driessche, P.V.D., Watmough, J., Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180(1)(2002), 29–48.
  • Ehrlich, I., On the usefulness of controlling individuals: An economic analysis of rehabilitation, incapacitation and deterrence, Amer. Econ. Rev., 71(3)(1981), 307–322.
  • FBI Report 2018, Crime in the Unite States, https://ucr.fbi.gov/crime-in-the-u.s/2018/crime-in-theu.s.-2018/topic-pages/nations-two-crimemeasures.
  • Glueck, S., Glueck, E.T., Unravelling Juvenile Delinquency, Harvard University Press, Cambridge, 1950.
  • Gordon, M.B., A random walk in the literature on criminality: A partial and critical view on some statistical analyses and modeling approaches, Eur. J. Appl. Math., 21(2010), 283–306.
  • Hethcote, H.W., The mathematics of infectious diseases, SIAM Rev., 42(4)(2000), 599–653.
  • Kretzschmar, M., Wallinga, J., Mathematical models in infectious disease epidemiology, Modern Infectious Disease Epidemiology, Springer New York, NY, (2009), 209–221.
  • Lawson, D., Marion, G., An Introduction to Mathematical Modeling, Bioinformatics and Statistics Scotland, 2008.
  • Lehrer, D., Trauma-informed care: The importance of understanding the incarcerated women, J. Correct. Health Care, 27(2)(2021), 121–126.
  • Martin, J.A., Hamilton, B.E., Osterman, M.J.K., Births in the United States, 2022, NCHS Data Brief, 477(2023).
  • McMillon, D., Simon, C.P., Morenoff, J., Modeling the underlying dynamics of the spread of crime, PLoS One, 9(4)(2014).
  • Santonja, F.J., Tarazona, A.C., Villanueva, R.J., A mathematical model of the pressure of an extreme ideology on a society, Comput. Math. Appl., 56(2008), 836–846.
  • Zhao, H., Feng, Z., Castillo-Chavez, C., The dynamics of poverty and crime, J. Shanghai Normal Univ., Nat. Sci. Math., 43(5)(2014), 486–495.
Year 2024, Volume: 16 Issue: 2, 481 - 489, 31.12.2024
https://doi.org/10.47000/tjmcs.1555972

Abstract

References

  • Ahmad, F.B., Cisewski, J.A., Xu, J., Anderson, R.N., Provisional mortality data - United States, 2022, Morb. Mortal. Wkly. Rep., 72(2023), 488–492.
  • Akers, R.L., Sellers, C.S., Criminological Theories: Introduction, Evaluation, and Application, Oxford University Press, 2012.
  • Alper, M., Durosse, M.R., Markman, J., Update on prisoner recidivism, NCJ 250975, (2018).
  • Banks, H.T., Castillo-Chavez, C., Bioterrorism: Mathematical modeling applications in homeland security, Society for Industrial and Applied Mathematics, Philadelphia, PA, 2003.
  • Becker, G., Crime and punishment: An economic approach, J. Politic. Econ. 76(1968), 169–217.
  • Brauer, F., Driessche, P.V.D., Models for transmission of disease with immigration of infectives, Math. Biosci., 171(1995), 143–154.
  • Carson, E.A., Mortality in state and federal prisons 2001-2019, U.S. Department of Justice, Office of Justice Programs Bureau of Justice Statistics, (2021).
  • Castillo-Chavez, C., Feng, Z., Huang, W., On the computation of R0 and its role on global stability, Mathematical Approaches for Emerging and Reemerging Infectious Diseases: An Introduction, Vol. 125 of IMA, Springer, New York, (2002), 229–250.
  • Clinard, M.B., Meier, R.F., Sociology of Deviant Behavior, Wadsworth, Belmont, 2010.
  • Crane, J., The epidemic theory of ghettos and neighborhood effects on dropping out and teenage childbearing, Amer. J. Sociol., 96(1991), 1226–1259.
  • Diekmann, O., Heesterbeek, J.A.P., Metz, J.A.J., On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations, J. Math. Biol., 28(4)(1990), 365–382.
  • Driessche, P.V.D., Watmough, J., Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180(1)(2002), 29–48.
  • Ehrlich, I., On the usefulness of controlling individuals: An economic analysis of rehabilitation, incapacitation and deterrence, Amer. Econ. Rev., 71(3)(1981), 307–322.
  • FBI Report 2018, Crime in the Unite States, https://ucr.fbi.gov/crime-in-the-u.s/2018/crime-in-theu.s.-2018/topic-pages/nations-two-crimemeasures.
  • Glueck, S., Glueck, E.T., Unravelling Juvenile Delinquency, Harvard University Press, Cambridge, 1950.
  • Gordon, M.B., A random walk in the literature on criminality: A partial and critical view on some statistical analyses and modeling approaches, Eur. J. Appl. Math., 21(2010), 283–306.
  • Hethcote, H.W., The mathematics of infectious diseases, SIAM Rev., 42(4)(2000), 599–653.
  • Kretzschmar, M., Wallinga, J., Mathematical models in infectious disease epidemiology, Modern Infectious Disease Epidemiology, Springer New York, NY, (2009), 209–221.
  • Lawson, D., Marion, G., An Introduction to Mathematical Modeling, Bioinformatics and Statistics Scotland, 2008.
  • Lehrer, D., Trauma-informed care: The importance of understanding the incarcerated women, J. Correct. Health Care, 27(2)(2021), 121–126.
  • Martin, J.A., Hamilton, B.E., Osterman, M.J.K., Births in the United States, 2022, NCHS Data Brief, 477(2023).
  • McMillon, D., Simon, C.P., Morenoff, J., Modeling the underlying dynamics of the spread of crime, PLoS One, 9(4)(2014).
  • Santonja, F.J., Tarazona, A.C., Villanueva, R.J., A mathematical model of the pressure of an extreme ideology on a society, Comput. Math. Appl., 56(2008), 836–846.
  • Zhao, H., Feng, Z., Castillo-Chavez, C., The dynamics of poverty and crime, J. Shanghai Normal Univ., Nat. Sci. Math., 43(5)(2014), 486–495.
There are 24 citations in total.

Details

Primary Language English
Subjects Dynamical Systems in Applications
Journal Section Articles
Authors

Michael Aguadze 0009-0001-0051-1180

Ana Vivas 0009-0008-8145-3605

Sujan Pant 0009-0002-4928-4860

Kubilay Dagtoros 0000-0002-8588-097X

Publication Date December 31, 2024
Submission Date September 25, 2024
Acceptance Date December 11, 2024
Published in Issue Year 2024 Volume: 16 Issue: 2

Cite

APA Aguadze, M., Vivas, A., Pant, S., Dagtoros, K. (2024). Spread of Crime Dynamics: A Mathematical Approach. Turkish Journal of Mathematics and Computer Science, 16(2), 481-489. https://doi.org/10.47000/tjmcs.1555972
AMA Aguadze M, Vivas A, Pant S, Dagtoros K. Spread of Crime Dynamics: A Mathematical Approach. TJMCS. December 2024;16(2):481-489. doi:10.47000/tjmcs.1555972
Chicago Aguadze, Michael, Ana Vivas, Sujan Pant, and Kubilay Dagtoros. “Spread of Crime Dynamics: A Mathematical Approach”. Turkish Journal of Mathematics and Computer Science 16, no. 2 (December 2024): 481-89. https://doi.org/10.47000/tjmcs.1555972.
EndNote Aguadze M, Vivas A, Pant S, Dagtoros K (December 1, 2024) Spread of Crime Dynamics: A Mathematical Approach. Turkish Journal of Mathematics and Computer Science 16 2 481–489.
IEEE M. Aguadze, A. Vivas, S. Pant, and K. Dagtoros, “Spread of Crime Dynamics: A Mathematical Approach”, TJMCS, vol. 16, no. 2, pp. 481–489, 2024, doi: 10.47000/tjmcs.1555972.
ISNAD Aguadze, Michael et al. “Spread of Crime Dynamics: A Mathematical Approach”. Turkish Journal of Mathematics and Computer Science 16/2 (December 2024), 481-489. https://doi.org/10.47000/tjmcs.1555972.
JAMA Aguadze M, Vivas A, Pant S, Dagtoros K. Spread of Crime Dynamics: A Mathematical Approach. TJMCS. 2024;16:481–489.
MLA Aguadze, Michael et al. “Spread of Crime Dynamics: A Mathematical Approach”. Turkish Journal of Mathematics and Computer Science, vol. 16, no. 2, 2024, pp. 481-9, doi:10.47000/tjmcs.1555972.
Vancouver Aguadze M, Vivas A, Pant S, Dagtoros K. Spread of Crime Dynamics: A Mathematical Approach. TJMCS. 2024;16(2):481-9.