Abstract
Optimal control is a powerful optimization technique derived from the mathematical theory of the Calculus of Variations. It can be employed to maximize the returns from and minimize the costs associated with physical, social, and economic processes. In recent years, optimal control theory has been utilized to develop ideal intervention strategies for a variety of “contagious” social ailments that are spread chiefly by contact with affected peers—like crime, substance abuse and the rampant infiltration of internet worms and viruses in computerized systems. As the dynamics of these processes are akin to that of an epidemic, the compartmental models utilized for studying the spread of infectious diseases can be easily adapted for these types of problems. In this article, we review the use of optimal control theory in the design of cost effective intervention strategies for the successful mitigation of social contagion processes.
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References
Camacho ET (2013) The development and interaction of terrorist and fanatic groups. Commun Nonlinear Sci Numer Simul 18(11):3086–3097
Campbell M, Ormerod P (1997) Social interaction and the dynamics of crime. Technical report, Volterra Consulting Ltd
Castillo-Chavez C, Song B (2003) Bioterrorism: mathematical modeling applications in homeland security. In: Banks HT, Castillo-Chavez C (eds) Chapter: Models for the transmission dynamics of fanatic behaviors. SIAM, Philadelphia, pp 55–172
Sooknanan J, Bhatt BS, Comissiong DMG (2013) 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(1):25–43
Sooknanan J, Bhatt BS, Comissiong DMG (2012) Life and death in a gang: a mathematical model of gang membership. J Math Res 4(4):10–27
Sooknanan J, Comissiong DMG (2017) A mathematical model for the treatment of delinquent behavior. Socio-Econ Plan Sci. https://doi.org/10.1016/j.seps.2017.08.001
Mushayabasa S (2017) Modeling optimal intervention strategies for property crime. Int J Dyn Control 5(3):832–841
Lee S, Jung E, Castillo-Chavez C (2010) Optimal control intervention strategies in low- and high-risk problem drinking populations. Socio-Econ Plan Sci 44(4):258–265
Mulone G, Straughan B (2012) Modeling binge drinking. Int J Biomath 05(01):1250005
Mushayabasa S (2015) The role of optimal intervention strategies on controlling excessive alcohol drinking and its adverse health effects. J Appl Math 2015, Article ID 238784
Kubo M, Naruse K, Sato H, Matubara T (2007) The possibility of an epidemic meme analogy for web community population analysis. Springer, Berlin, pp 1073–1080
Woo J, Chen H (2016) Epidemic model for information diffusion in web forums: experiments in marketing exchange and political dialog. SpringerPlus 5:66
Kribs-Zaleta CM (2013) Sociological phenomena as multiple nonlinearities: MTBI’s new metaphor for complex human interactions. Math Biosci Eng 10(5–6):1587–1607
Buonomo B, Lacitgnola D, Vargas-De-Leon C (2014) Qualitative analysis and optimal control of an epidemic model with vaccination and treatment. Math Comput Simul 100:88–102
Di Liddo A (2016) Optimal control and treatment of infectious diseases. The case of huge treatment costs. Mathematics 4(2):21
Sharomi O, Malik T (2015) Optimal control in epidemiology. Ann Oper Res 1(251):55–71
Hansen E, Day T (2011) Optimal control of epidemics with limited resources. J Math Biol 62(3):423–451
Rowthorn R, Walther S (2017) The optimal treatment of an infectious disease with two strains. J Math Biol 74(7):1753–1791
Grass D, Caulkins JP, Feichetinger G, Tragler G, Behrens DA (2008) Optimal control of nonlinear processes with applications in drugs, corruption and terror. Springer, Berlin
Sargent RWH (2000) Optimal control. J Comput Appl Math 124:361–371
Nie T, Shi J, Wu Z (2016) Connection between MP and DPP for stochastic recursive optimal control processes: viscosity solution framework in local case. In: American control conference (ACC). IEEE, Boston, pp 7225–7230
Fleming WH, Rishel RW (1975) Deterministic and stochastic optimal control. Springer, New York
Pontryagin LS, Boltyanskii VT, Gamkrelidze RV, Mishcheuko EF (1962) The mathematical theory of optimal control processes. Wiley, New York
Babor T (2010) Drug policy and the public good. Oxford University Press, Oxford
Reuter P (2006) What drug policies cost: estimating government drug policy expenditures. Addiction 101(3):315–322
United Nations Office on Drugs and Crime, International Standards on Drug Use Prevention, online. https://www.unodc.org/documents/prevention/UNODC_2013_2015_international_standards_on_drug_use_prevention_E.pdf
Rosenquist JN, Murabito J, Fowler JH, Christakis NA (2010) The spread of alcohol consumption behavior in a large social network. Ann Intern Med 152(7):426–433
Studer J, Baggio S, Deline S, N’Goran AA, Henchoz Y, Mohler-Kuo M, Daeppen J, Gmel G (2014) Peer pressure and alcohol use in young men: a mediation analysis of drinking motives. Int J Drug Policy 25(4):700–708
Strickland JC, Smith MA (2014) The effects of social contact on drug use: behavioral mechanisms controlling drug intake. Exp Clin Psychopharmacol 22(1):23–34
Choi S, Lee J, Jung E (2014) Optimal strategies for prevention of ecstasy use. J Korea Soc Ind Appl Math 18(1):1–15
Song B, Castillo-Chavez C (2006) Raves, clubs and ecstasy: the impact of peer pressure. Math Biosci Eng 3(1):249–266
Mushayabasa S, Tapedzesa G (2015) Modeling illicit drug use dynamics and its optimal control analysis. Comput Math Methods Med 2015, Article ID 383154, 11 pages
Haw C, Hawton K, Houston K, Townsend E (2001) Psychiatric and personality disorders in deliberate self-harm patients. Br J Psychiatry 178(1):48–54
Muehlenkamp JJ, Claes L, Havertape L, Plener PL (2012) International prevalence of adolescent non-suicidal self-injury and deliberate self-harm. Child Adolesc Psychiatry Mental Health 6(1):10
Kim BN, Masud MA, Kim Y (2014) Optimal implementation of intervention to control the self-harm epidemic. Osong Public Health Res Perspect 5(6):315–323
Christakis NA, Fowler JH (2007) The spread of obesity in a large social network over 32 years. New Engl J Med 357(4):370–379
Oh C, Masud MA (2015) Optimal intervention strategies for the spread of obesity. J Appl Math 2015, Article ID 217808, 9 pages
Aldila D, Rarasati N, Nuraini N, Soewono E (2014) Optimal control problem of treatment for obesity in a closed population. Int J Math Math Sci 2014, Article ID 273037, 7 pages
CBS News Report, Online (2017). https://www.cbsnews.com/news/wannacry-ransomware-attacks-wannacry-virus-losses/security-7252335/?ito=cbshare
Yan X, Zou Y (2008) Optimal internet worm treatment strategy based on the two-factor model. ETRI J 30(1):81–88
Chen L, Hattaf K, Sun J (2015) Optimal control of a delayed SLBS computer virus model. Physica A 427:244–250
Bi J, Yang X, Wu Y, Xiong Q, Wen J, Tang YY (2017) On the optimal dynamic control strategy of disruptive computer virus. Discrete Dyn Nat Soc 2017, Article ID 8390784, 14 pages
Kandhway K, Kuri J (2014) Optimal control of information epidemics modeled as Maki Thompson rumors. Commun Nonlinear Sci Numer Simul 19(12):4135–4147
Huo L, Lin T, Fan C, Liu C, Zhao J (2015) Optimal control of a rumor propagation model with latent period in emergency event. Adv Differ Equ 2015:54
Sooknanan J, Bhatt BS, Comissiong DMG (2013) Another way of thinking: A review of mathematical models of crime. Math Today 131–133. https://pdfs.semanticscholar.org/8d68/5fa908f1fc4933adf8d4806bf82035bc427e.pdf
Castillo-Chavez C, Lee S (2011) Epidemiology modeling in the life and social sciences, CiteSeerX
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Comissiong, D.M.G., Sooknanan, J. A review of the use of optimal control in social models. Int. J. Dynam. Control 6, 1841–1846 (2018). https://doi.org/10.1007/s40435-018-0405-3
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DOI: https://doi.org/10.1007/s40435-018-0405-3