More Web Proxy on the site http://driver.im/

research-article

Dynamic Computational Models and Simulations of the Opioid Crisis: A Comprehensive Survey

Authors:

Narjes Shojaati,

Nathaniel D. OsgoodAuthors Info & Claims

ACM Transactions on Computing for Healthcare (HEALTH), Volume 3, Issue 1

Article No.: 13, Pages 1 - 25

https://doi.org/10.1145/3469658

Published: 15 October 2021 Publication History

Abstract

Opioids have been shown to temporarily reduce the severity of pain when prescribed for medical purposes. However, opioid analgesics can also lead to severe adverse physical and psychological effects or even death through misuse, abuse, short- or long-term addiction, and one-time or recurrent overdose. Dynamic computational models and simulations can offer great potential to interpret the complex interaction of the drivers of the opioid crisis and assess intervention strategies. This study surveys existing studies of dynamic computational models and simulations addressing the opioid crisis and provides an overview of the state-of-the-art of dynamic computational models and simulations of the opioid crisis. This review gives a detailed analysis of existing modeling techniques, model conceptualization and formulation, and the policy interventions they suggest. It also explores the data sources they used and the study population they represented. Based on this analysis, direction and opportunities for future dynamic computational models for addressing the opioid crisis are suggested.

References

[1]

Mohamed Abdou, Lynne Hamill, and Nigel Gilbert. 2012. Designing and building an agent-based model. In Agent-based Models of Geographical Systems. Springer, 141–165.

[2]

Michael Agar. 2003. My kingdom for a function: Modeling misadventures of the innumerate. J. Artif. Soc. Soc. Simul 6, 3 (2003), 11.

[3]

Michael H. Agar. 2001. Another complex step: A model of heroin experimentation. Field Methods 13, 4 (2001), 353–369. DOI:https://doi.org/10.1177/1525822X0101300403

[4]

Michael H. Agar and Dwight Wilson. 2002. Drugmart: Heroin epidemics as complex adaptive systems. Complexity 7, 5 (2002), 44–52. DOI:https://doi.org/10.1002/cplx.10040

Digital Library

[5]

Michael Agar and Heather Schacht Reisinger. 2000. Explaining Drug Use Trends Suburban Heroin Use in Baltimore County. na.

[6]

Michael Agar and Heather Schacht Reisinger. 2001. Using trend theory to explain heroin use trends. J. Psychoactive Drugs 33, 3 (2001), 203–211. DOI:https://doi.org/10.1080/02791072.2001.10400567

[7]

Roy M. Anderson, B. Anderson, and Robert M. May. 1992. Infectious Diseases of Humans: Dynamics and Control. Oxford University Press.

[8]

Victoria Barnett, Robert Twycross, Mary Mihalyo, and Andrew Wilcock. 2014. Opioid antagonists. J. Pain Symptom Manage 47, 2 (2014), 341–352. DOI:https://doi.org/10.1016/j.jpainsymman.2013.12.223

[9]

Nicholas A. Battista. 2015. A comparison of heroin epidemic models. ArXiv151004581 Q-Bio (2015). Retrieved May 6, 2020 from http://arxiv.org/abs/1510.04581.

[10]

Nicholas A. Battista, Leigh B. Pearcy, and W. Christopher Strickland. 2019. Modeling the prescription opioid epidemic. Bull. Math. Biol 81, 7 (2019), 2258–2289. DOI:https://doi.org/10.1007/s11538-019-00605-0

[11]

Jöran Beel and Bela Gipp. 2009. Google scholar's ranking algorithm: The impact of articles’ age (an empirical study). In 2009 Sixth International Conference on Information Technology: New Generations, IEEE, Las Vegas, NV, USA, 160–164. DOI:https://doi.org/10.1109/ITNG.2009.317

Digital Library

[12]

Georgiy Bobashev, Sam Goree, Jennifer Frank, and William Zule. 2018. Pain town, an agent-based model of opioid use trajectories in a small community. In Social, Cultural, and Behavioral Modeling, Robert Thomson, Christopher Dancy, Ayaz Hyder, and Halil Bisgin (eds.). Springer International Publishing, Cham, 274–285. DOI:https://doi.org/10.1007/978-3-319-93372-6_31

[13]

Georgiy Bobashev, Eric Solano, and Lee Hoffer. 2014. An agent-based model to support measuring drug choice and switch between drug types in rural populations. In SIMUL, The Sixth International Conference on Advances in System Simulation, Citeseer.

[14]

Fred Brauer. 2008. Compartmental models in epidemiology. In Mathematical Epidemiology. Springer, 19–79.

[15]

E. Chattoe, M. Hickman, and P. Vickerman. 2005. Drugs futures 2025? Modelling drug use. Foresight (2005), 48–68.

[16]

Mersha Chetty, James J. Kenworthy, Sue Langham, Andrew Walker, and William C. N. Dunlop. 2017. A systematic review of health economic models of opioid agonist therapies in maintenance treatment of non-prescription opioid dependence. Addict. Sci. Clin. Pract. 12, 1 (2017), 6. DOI:https://doi.org/10.1186/s13722-017-0071-3

[17]

Kao-Ping Chua, Chad M. Brummett, Rena M. Conti, Rebecca L. Haffajee, Lisa A. Prosser, and Amy SB Bohnert. 2019. Assessment of prescriber and pharmacy shopping among the family members of patients prescribed opioids. JAMA Netw. Open 2, 5 (2019), e193673–e193673.

[18]

Shane Darke, Joanne Ross, Katherine L. Mills, Anna Williamson, Alys Havard, and Maree Teesson. 2007. Patterns of sustained heroin abstinence amongst long-term, dependent heroin users: 36 months findings from the Australian treatment outcome study (ATOS). Addict. Behav. 32, 9 (2007), 1897–1906. DOI:https://doi.org/10.1016/j.addbeh.2007.01.014

[19]

Louisa Degenhardt, Carolyn Day, Stuart Gilmour, and Wayne Hall. 2006. The “lessons” of the Australian “heroin shortage.” Subst. Abuse Treat. Prev. Policy 1, 1 (2006), 11. DOI:https://doi.org/10.1186/1747-597X-1-11

[20]

Sarah DeWeerdt. 2019. Tracing the US opioid crisis to its roots. Nature 573, 7773 (2019), S10–S10.

[21]

Paul Dietz, Australasian Centre for Policing Research, and National Drug Law Enforcement Research Fund (Australia). 2004. The Course and Consequences of the Heroin Shortage in Victoria. Australasian Centre for Policing Research: National Drug Law Enforcement Research Fund, Payneham, S.A.

[22]

Anne Dray, Lorraine Mazerolle, Pascal Perez, and Alison Ritter. 2008. Drug law enforcement in an agent-based model: Simulating the disruption to street-level drug markets. In Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems. IGI Global, 352–371.

[23]

Anne Dray, Lorraine Mazerolle, Pascal Perez, and Alison Ritter. 2008. Policing Australia's ‘heroin drought’: Using an agent-based model to simulate alternative outcomes. J. Exp. Criminol. 4, 3 (2008), 267–287. DOI:https://doi.org/10.1007/s11292-008-9057-1

[24]

Gabor Egervari, Roberto Ciccocioppo, J. David Jentsch, and Yasmin L. Hurd. 2018. Shaping vulnerability to addiction–the contribution of behavior, neural circuits and molecular mechanisms. Neurosci. Biobehav. Rev. 85, (2018), 117–125.

[25]

Leslie R. Foulds. 2012. Graph Theory Applications. Springer Science & Business Media.

[26]

Jose Manuel Galan and Luis R Izquierdo. 2005. Appearances can be deceiving: Lessons learned re-implementing Axelrod's’ evolutionary approach to norms’. J. Artif. Soc. Soc. Simul. 8, 3 (2005).

[27]

Samuel Gatley. 2017. The impact of prescription drug monitoring programs on the dynamics of the opioid epidemic. Rochester Institute of Technology.

[28]

Nigel Gilbert and Klaus Troitzsch. 2005. Simulation for the Social Scientist. McGraw-Hill Education (UK).

Digital Library

[29]

Tara Gomes, Mina Tadrous, Muhammad M. Mamdani, J. Michael Paterson, and David N. Juurlink. 2018. The burden of opioid-related mortality in the United States. JAMA Netw. Open 1, 2 (2018), e180217–e180217.

[30]

Walter R. Gove. 1975. The labelling theory of mental illness: A reply to Scheff. Am. Sociol. Rev. 40, 2 (1975), 242–248.

[31]

Daniel Heard, Georgiy V. Bobashev, and Robert J. Morris. 2014. Reducing the complexity of an agent-based local heroin market model. PLoS ONE 9, 7 (2014), e102263. DOI:https://doi.org/10.1371/journal.pone.0102263

[32]

Holly Hedegaard, Margaret Warner, and Arialdi M. Miniño. 2017. Drug overdose deaths in the United States, 1999–2015. (2017).

[33]

Michaela Hiebler-Ragger and Human-Friedrich Unterrainer. 2019. The role of attachment in poly-drug use disorder: An overview of the literature, recent findings and clinical implications. Front. Psychiatry 10, (2019), 579. DOI:https://doi.org/10.3389/fpsyt.2019.00579

[34]

Lee Hoffer. 2013. Unreal models of real behavior: The agent-based modeling experience. Pract. Anthropol. 35, 1 (2013), 19–23. DOI:https://doi.org/10.17730/praa.35.1.2hn672m0158u5152

[35]

Lee Hoffer and Shah Jamal Alam. 2013. “Copping” in heroin markets: The hidden information costs of indirect sales and why they matter. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, Springer, 83–92.

Digital Library

[36]

Lee Hoffer, Georgiy Bobashev, and Robert J. Morris. 2012. Simulating patterns of heroin addiction within the social context of a local heroin market. In Computational Neuroscience of Drug Addiction. Springer, 313–331.

[37]

Lee D. Hoffer. 2006. Junkie Business: The Evolution and Operation of a Heroin Dealing Network. Thomson/Wadsworth, Australia; Belmont, CA.

[38]

Lee D. Hoffer, Georgiy Bobashev, and Robert J. Morris. 2009. Researching a local heroin market as a complex adaptive system. Am. J. Community Psychol. 44, 3–4 (2009), 273.

[39]

Lee Hogarth. 2018. A critical review of habit theory of drug dependence. Psychol. Habit (2018), 325–341.

[40]

Carl I. Hovland and Walter Weiss. 1951. The influence of source credibility on communication effectiveness. PUBLIC Opin. Q. 15, 4 (1951), 16.

[41]

Mohammad S. Jalali, Michael Botticelli, Rachael C. Hwang, Howard K. Koh, and R. Kathryn McHugh. 2020. The opioid crisis: Need for systems science research. Health Res. Policy Syst. 18, 1 (2020), 1–5.

[42]

Christopher M. Jones, Joseph Logan, R. Matthew Gladden, and Michele K. Bohm. 2015. Vital Signs: Demographic and substance use trends among heroin users — United States, 2002–2013. 64, 26 (2015), 7.

[43]

Johan Kakko, Hannu Alho, Alexander Baldacchino, Rocío Molina, Felice Alfonso Nava, and Gabriel Shaya. 2019. Craving in opioid use disorder: From neurobiology to clinical practice. Front. Psychiatry 10, (2019), 592.

[44]

Kunal Karamchandani, John C. Klick, Melissa Linskey Dougherty, Anthony Bonavia, Steven R. Allen, and Zyad J. Carr. 2019. Pain management in trauma patients affected by the opioid epidemic: A narrative review. J. Trauma Acute Care Surg. 87, 2 (2019), 430–439.

[45]

Christopher Keane, James E. Egan, and Mary Hawk. 2018. Effects of naloxone distribution to likely bystanders: Results of an agent-based model. Int. J. Drug Policy 55, (2018), 61–69. DOI:https://doi.org/10.1016/j.drugpo.2018.02.008

[46]

William Ogilvy Kermack and Anderson G. McKendrick. 1927. A contribution to the mathematical theory of epidemics. Proc. R. Soc. Lond. Ser. Contain. Pap. Math. Phys. Character 115, 772 (1927), 700–721.

[47]

Thomas R. Kosten and Tony P. George. 2002. The neurobiology of opioid dependence: Implications for treatment. Sci. Pract. Perspect. 1, 1 (2002), 13.

[48]

Christian Kreibich. 2015. A Parser for Google Scholar, written in Python.

[49]

Averill M. Law, W. David Kelton, and W. David Kelton. 2000. Simulation Modeling and Analysis. McGraw-Hill New York.

Digital Library

[50]

Dan J. Lettieri. 1985. Drug Abuse: A review of explanations and models of explanation. Adv. Alcohol Subst. Abuse 4, 3–4 (1985), 9–40. DOI:https://doi.org/10.1300/J251v04n03_02

[51]

Dan J. Lettieri, Mollie Sayers, and Helen Wallenstein Pearson. 1980. Theories on Drug Abuse: Selected Contemporary Perspectives. US Department of Health and Human Services, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute on Drug Abuse, Division of Research.

[52]

Belzak Lisa and Halverson Jessica. 2018. Evidence synthesis-the opioid crisis in Canada: A national perspective. Health Promot. Chronic Dis. Prev. Can. Res. Policy Pract. 38, 6 (2018), 224.

[53]

Freerk A. Lootsma. 2013. Fuzzy Logic for Planning and Decision Making. Springer Science & Business Media.

[54]

Kate R. Lorig and Halsted R. Holman. 2003. Self-management education: History, definition, outcomes, and mechanisms. Ann. Behav. Med. 26, 1 (2003), 1–7.

[55]

Mingju Ma, Sanyang Liu, and Jun Li. 2017. Bifurcation of a heroin model with nonlinear incidence rate. Nonlinear Dyn. 88, 1 (2017), 555–565. DOI:https://doi.org/10.1007/s11071-016-3260-9

[56]

Mingju Ma, Sanyang Liu, and Jun Li. 2017. Does media coverage influence the spread of drug addiction? Commun. Nonlinear Sci. Numer. Simul. 50, (2017), 169–179. DOI:https://doi.org/10.1016/j.cnsns.2017.03.002

[57]

Bertha K. Madras. 2017. The surge of opioid use, addiction, and overdoses: Responsibility and response of the US health care system. JAMA Psychiatry 74, 5 (2017), 441. DOI:https://doi.org/10.1001/jamapsychiatry.2017.0163

[58]

Elinore F. McCance-Katz. 2018. The Substance Abuse and Mental Health Services Administration (SAMHSA): New directions. Psychiatr. Serv. 69, 10 (2018), 1046–1048.

[59]

Daniel McNeill and Paul Freiberger. 1994. Fuzzy logic: The Revolutionary Computer Technology that is Changing Our World. Simon and Schuster.

Digital Library

[60]

Sararose Nassani. 2019. An Application of Statistics and Random Graphs to Analyze Local Heroin Markets. Case Western Reserve University.

[61]

Engineering National Academies of Sciences. 2017. Pain management and the opioid epidemic: Balancing societal and individual benefits and risks of prescription opioid use. (2017).

[62]

Anh P. Nguyen, Jason M. Glanz, Komal J. Narwaney, and Ingrid A. Binswanger. 2020. Association of opioids prescribed to family members with opioid overdose among adolescents and young adults. JAMA Netw. Open 3, 3 (2020), e201018–e201018.

[63]

Alexandra Nielsen and Wayne Wakeland. 2014. Dynamic simulation of the effect of tamper resistance on opioid misuse outcomes. In Simulation and Modeling Methodologies, Technologies and Applications. Springer International Publishing, Cham, 169–181. DOI:https://doi.org/10.1007/978-3-319-03581-9_12

[64]

Alexandra Nielsen, Wayne Wakeland, and Teresa Schmidt. 2013. An epidemic model of nonmedical opioid use with simulated public health interventions. In Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SciTePress - Science and Technology Publications, Reykjavík, Iceland, 556–564. DOI:https://doi.org/10.5220/0004621905560564

[65]

Hatson John Boscoh Njagarah and Farai Nyabadza. 2013. Modeling the impact of rehabilitation, amelioration and relapse on the prevalence of drug epidemics. J. Biol. Syst. 21, 01 (2013), 1350001. DOI:https://doi.org/10.1142/S0218339013500010

[66]

Guy H. Orcutt. 2007. A new type of socio-economic system. Int. J. Microsimulation 1, 1 (2007), 2–9.

[67]

Bryce Pardo, Lois M. Davis, and Melinda Moore. 2019. Characterization of the Synthetic Opioid Threat Profile to Inform Inspection and Detection Solutions. Homeland Security Operational Analysis Center (HSOAC), RAND CORP Santa ….

[68]

Bryce Pardo, Jirka Taylor, Jonathan Caulkins, Beau Kilmer, Peter Reuter, and Bradley Stein. 2019. The Future of Fentanyl and Other Synthetic Opioids. RAND Corporation. DOI:https://doi.org/10.7249/RR3117

[69]

Pascal Perez, Anne Dray, and Alison Ritter. 2005. SimDrug: Exploring the Complexity of Heroin use in Melbourne. Turning Point Alcohol and Drug Centre, Australian National University, and Griffith University, Fitzroy, Vic.

[70]

Richard E. Petty. 1986. The Elaboration Likelihood Model of Persuasion. In Communication and Persuasion. Springer, 1–24.

[71]

Jonathan K. Phillips, Morgan A. Ford, and Richard J. Bonnie (Eds.). 2017. Pain Management and the Opioid Epidemic: Balancing Societal and Individual Benefits and Risks of Prescription Opioid Use. National Academies Press (US), Washington (DC). DOI:https://doi.org/10.17226/24781

[72]

Allison L. Pitt, Keith Humphreys, and Margaret L. Brandeau. 2018. Modeling health benefits and harms of public policy responses to the US opioid epidemic. Am. J. Public Health 108, 10 (2018), 1394–1400. DOI:https://doi.org/10.2105/AJPH.2018.304590

[73]

Alison Ritter, Nagesh Shukla, Marian Shanahan, Phuong Van Hoang, Vu Lam Cao, Pascal Perez, and Michael Farrell. 2015. Building a microsimulation model of heroin use careers in Australia. Int. J. Microsimulation 9, 3 (2015), 140–176. DOI:https://doi.org/10.34196/ijm.00146

[74]

Everett M. Rogers. 2002. Diffusion of preventive innovations. Addict. Behav. 27, 6 (2002), 989–993. DOI:https://doi.org/10.1016/S0306-4603(02)00300-3

[75]

Catherine O. Samuel-Ojo. 2015. The influence of pain self-management education on the prevalence of opioid prescription among patients with chronic non-cancer pain: An agent-based modeling simulation. (2015), 112.

[76]

Hiroki Sayama. 2015. Introduction to the modeling and analysis of complex systems. Open SUNY Textbooks.

[77]

Ty S. Schepis (Ed.). 2018. The Prescription Drug Abuse Epidemic: Incidence, Treatment, Prevention, and Policy. Praeger, an imprint of ABC-CLIO, LLC, Santa Barbara, California.

[78]

Elizabeth Y. Schiller, Amandeep Goyal, and Oren J. Mechanic. 2017. Opioid overdose. (2017).

[79]

Ralf W. Schlosser, Oliver Wendt, Suresh Bhavnani, and Barbara Nail-Chiwetalu. 2006. Use of information-seeking strategies for developing systematic reviews and engaging in evidence-based practice: The application of traditional and comprehensive pearl growing. A review. Int. J. Lang. Commun. Disord. 41, 5 (2006), 567–582. DOI:https://doi.org/10.1080/13682820600742190

[80]

Teresa Schmidt, Amanuel Zimam, Alexandra Nielsen, and Wayne Wakeland. 2014. Data sources regarding the nonmedical use of pharmaceutical opioids in the United States. Rev. Health Care 5, 1 (2014), 33.

[81]

Marissa J. Seamans, Timothy S. Carey, Daniel J. Westreich, Stephen R. Cole, Stephanie B. Wheeler, G. Caleb Alexander, Virginia Pate, and M. Alan Brookhart. 2018. Association of household opioid availability and prescription opioid initiation among household members. JAMA Intern. Med. 178, 1 (2018), 102–109.

[82]

Nasser Sharareh, Shabnam S Sabounchi, Mary McFarland, and Rachel Hess. 2019. Evidence of modeling impact in development of policies for controlling the opioid epidemic and improving public health: A scoping review. Subst. Abuse Res. Treat. 13, (2019), 117822181986621. DOI:https://doi.org/10.1177/1178221819866211

[83]

Nagesh Shukla, Van Hoang, Marian Shahanan, Alison Ritter, Vu Lam Cao, and Pascal Perez. 2014. A lifetime individual sampling model (ISM) for heroin use and treatment evaluation in Australia. (2014). DOI:https://doi.org/10.13140/2.1.1833.4404

[84]

John Sterman. 2018. System Dynamics at Sixty: The Path Forward. Wiley Online Library.

[85]

John D. Sterman. 2001. System dynamics modeling: Tools for learning in a complex world. Calif. Manage. Rev. 43, 4 (2001), 8–25.

[86]

Sheena Taha, Bridget Maloney-Hall, and Jane Buxton. 2019. Lessons learned from the opioid crisis across the pillars of the Canadian drugs and substances strategy. Subst. Abuse Treat. Prev. Policy 14, 1 (2019), 32. DOI:https://doi.org/10.1186/s13011-019-0220-7

[87]

Amos Tversky and Daniel Kahneman. 1974. Judgment under uncertainty: Heuristics and biases. Science 185, 4157 (1974), 1124–1131.

[88]

Wayne Wakeland and Alexandra Nielsen. 2013. Modeling opioid addiction treatment policies using system dynamics. In 31st International System Dynamics Society Conference.

[89]

Wayne Wakeland, Alexandra Nielsen, and Peter Geissert. 2015. Dynamic model of nonmedical opioid use trajectories and potential policy interventions. Am. J. Drug Alcohol Abuse 41, 6 (2015), 508–518. DOI:https://doi.org/10.3109/00952990.2015.1043435

[90]

Wayne Wakeland, Alexandra Nielsen, and Teresa D. Schmidt. 2012. System Dynamics Modeling of Medical Use, Nonmedical Use and Diversion of Prescription Opioid Analgesics. St. Gallen, Switzerland, 23.

[91]

Wayne Wakeland, Alexandra Nielsen, and Teresa D. Schmidt. 2016. Gaining policy insight with a system dynamics model of pain medicine prescribing, diversion and abuse: Pain medicine policy model. Syst. Res. Behav. Sci. 33, 3 (2016), 400–412. DOI:https://doi.org/10.1002/sres.2345

[92]

Wayne Wakeland, Alexandra Nielsen, Teresa D. Schmidt, Dennis McCarty, Lynn R. Webster, John Fitzgerald, and J. David Haddox. 2013. Modeling the impact of simulated educational interventions on the use and abuse of pharmaceutical opioids in the United States: A report on initial efforts. Health Educ. Behav. 40, 1_suppl (2013), 74S–86S. DOI:https://doi.org/10.1177/1090198113492767

[93]

Wayne Wakeland, Teresa D. Schmidt, and J. David Haddox. 2011. A system dynamics model of pharmaceutical opioids: Medical use, diversion, and nonmedical use. Citeseer, 29.

[94]

Wayne Wakeland, Teresa Schmidt, Aaron M. Gilson, J. David Haddox, and Lynn R. Webster. 2011. System dynamics modeling as a potentially useful tool in analyzing mitigation strategies to reduce overdose deaths associated with pharmaceutical opioid treatment of chronic pain. Pain Med. 12, suppl 2 (2011), S49–S58. DOI:https://doi.org/10.1111/j.1526-4637.2011.01127.x

[95]

Isaac Mwangi Wangari and Lewi Stone. 2017. Analysis of a heroin epidemic model with saturated treatment function. J. Appl. Math. 2017, (2017), 1–21. DOI:https://doi.org/10.1155/2017/1953036

[96]

Lynn R. Webster. 2017. Risk factors for opioid-use disorder and overdose. Anesth. Analg. 125, 5 (2017), 1741–1748. DOI:https://doi.org/10.1213/ANE.0000000000002496

[97]

Emma White and Catherine Comiskey. 2007. Heroin epidemics, treatment and ODE modelling. Math. Biosci. 208, 1 (2007), 312–324. DOI:https://doi.org/10.1016/j.mbs.2006.10.008

[98]

J. Deanna Wilson, Kaleab Z. Abebe, Kevin Kraemer, Jane Liebschutz, Jessica Merlin, Elizabeth Miller, David Kelley, and Julie Donohue. 2021. Trajectories of opioid use following first opioid prescription in opioid-naive youths and young adults. JAMA Netw. Open 4, 4 (2021), e214552–e214552.

[99]

David A. Wiss. 2019. A biopsychosocial overview of the opioid crisis: Considering nutrition and gastrointestinal health. Front. Public Health 7, (2019), 193.

[100]

Junyuan Yang, Xiaoxia Li, and Fengqin Zhang. 2016. Global dynamics of a heroin epidemic model with age structure and nonlinear incidence. Int. J. Biomath. 09, 03 (2016), 1650033. DOI:https://doi.org/10.1142/S1793524516500339

[101]

Gary A. Zarkin, Laura J. Dunlap, Katherine A. Hicks, and Daniel Mamo. 2005. Benefits and costs of methadone treatment: Results from a lifetime simulation model. Health Econ. 14, 11 (2005), 1133–1150. DOI:https://doi.org/10.1002/hec.999

[102]

Results from the 2009 National Survey on Drug Use and Health: Volume I. Summary of National Findings. 104.

Cited By

Gariepy GZahan ROsgood NYeoh BGraham EOrpana H(2024)Dynamic Simulation Models of Suicide and Suicide-Related Behaviors: Systematic ReviewJMIR Public Health and Surveillance10.2196/6319510(e63195)Online publication date: 2-Dec-2024
https://doi.org/10.2196/63195
Tatara EOzik JPollack HSchneider JFriedman SHarawa NBoodram BSalisbury-Afshar EHotton AOuellet LMackesy-Amiti MCollier NMacal C(2024)Agent-Based Model of Combined Community- and Jail-Based Take-Home Naloxone DistributionJAMA Network Open10.1001/jamanetworkopen.2024.487327:12(e2448732)Online publication date: 10-Dec-2024
https://doi.org/10.1001/jamanetworkopen.2024.48732
Shojaati NOsgood N(2023)Opioid-related harms and care impacts of conventional and AI-based prescription management strategies: insights from leveraging agent-based modeling and machine learningFrontiers in Digital Health10.3389/fdgth.2023.11748455Online publication date: 20-Jun-2023
https://doi.org/10.3389/fdgth.2023.1174845

Index Terms

Dynamic Computational Models and Simulations of the Opioid Crisis: A Comprehensive Survey
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

Recommendations

Curbing the Opioid Crisis: Optimal Dynamic Policies for Preventive and Mitigating Interventions
Problem statement: This paper addresses the challenge of effectively responding to the opioid epidemic stemming from prescription pills through a public health lens. It centers on the strategic distribution of resources across diverse interventions aimed ...
Modeling the dynamics of dengue fever
SBP'13: Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction

Dengue is a major international public health concern that impacts one-third of the world's population. There are four serotypes of the dengue virus (DENV). Infection with one serotype affords life-long immunity to that serotype but only temporary cross ...
Transformation of potential medical demand in China

Display Omitted SD model was firstly used to analyze and predict PMD problem with historical data.SD model overcame limitations of statistical analysis, policy effect was tested.The results were in accordance with previous studies and the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 3, Issue 1

January 2022

255 pages

EISSN:2637-8051

DOI:10.1145/3485154

Editors:
Insup Lee
University of Pennsylvania, USA
,
John A. Stankovic
University of Virginia, USA

Issue’s Table of Contents

Copyright © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2021

Accepted: 01 June 2021

Revised: 01 May 2021

Received: 01 February 2021

Published in HEALTH Volume 3, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
452
Total Downloads

Downloads (Last 12 months)125
Downloads (Last 6 weeks)37

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gariepy GZahan ROsgood NYeoh BGraham EOrpana H(2024)Dynamic Simulation Models of Suicide and Suicide-Related Behaviors: Systematic ReviewJMIR Public Health and Surveillance10.2196/6319510(e63195)Online publication date: 2-Dec-2024
https://doi.org/10.2196/63195
Tatara EOzik JPollack HSchneider JFriedman SHarawa NBoodram BSalisbury-Afshar EHotton AOuellet LMackesy-Amiti MCollier NMacal C(2024)Agent-Based Model of Combined Community- and Jail-Based Take-Home Naloxone DistributionJAMA Network Open10.1001/jamanetworkopen.2024.487327:12(e2448732)Online publication date: 10-Dec-2024
https://doi.org/10.1001/jamanetworkopen.2024.48732
Shojaati NOsgood N(2023)Opioid-related harms and care impacts of conventional and AI-based prescription management strategies: insights from leveraging agent-based modeling and machine learningFrontiers in Digital Health10.3389/fdgth.2023.11748455Online publication date: 20-Jun-2023
https://doi.org/10.3389/fdgth.2023.1174845

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View full text|Download PDF

View Issue’s Table of Contents