1932

Abstract

Agent-based modeling is a computational approach in which agents with a specified set of characteristics interact with each other and with their environment according to predefined rules. We review key areas in public health where agent-based modeling has been adopted, including both communicable and noncommunicable disease, health behaviors, and social epidemiology. We also describe the main strengths and limitations of this approach for questions with public health relevance. Finally, we describe both methodologic and substantive future directions that we believe will enhance the value of agent-based modeling for public health. In particular, advances in model validation, comparisons with other causal modeling procedures, and the expansion of the models to consider comorbidity and joint influences more systematically will improve the utility of this approach to inform public health research, practice, and policy.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-publhealth-040617-014317
2018-04-01
2024-04-23
Loading full text...

Full text loading...

/deliver/fulltext/publhealth/39/1/annurev-publhealth-040617-014317.html?itemId=/content/journals/10.1146/annurev-publhealth-040617-014317&mimeType=html&fmt=ahah

Literature Cited

  1. Auchincloss AH, Diez Roux AV. 1.  2008. A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health. Am. J. Epidemiol. 168:11–8 [Google Scholar]
  2. Auchincloss AH, Garcia LMT. 2.  2015. Brief introductory guide to agent-based modeling and an illustration from urban health research. Cad. Saude Publica 31:Suppl. 165–78 [Google Scholar]
  3. Auchincloss AH, Riolo RL, Brown DG, Cook J, Diez Roux AV. 3.  2011. An agent-based model of income inequalities in diet in the context of residential segregation. Am. J. Prev. Med. 40:3303–11 [Google Scholar]
  4. Axelrod R. 4.  1997. Advancing the art of simulation in the social sciences. Simulating Social Phenomena R Conte, R Hegselmann, P Terna 21–40 Berlin: Springer [Google Scholar]
  5. Badland H, White M, Macaulay G, Eagleson S, Mavoa S. 5.  et al. 2013. Using simple agent-based modeling to inform and enhance neighborhood walkability. Int. J. Health Geogr. 12:58 [Google Scholar]
  6. Blok DJ, de Vlas SJ, Bakker R, van Lenthe FJ. 6.  2015. Reducing income inequalities in food consumption: explorations with an agent-based model. Am. J. Prev. Med. 49:4605–13 [Google Scholar]
  7. Bonabeau E. 7.  2002. Agent-based modeling: methods and techniques for simulating human systems. PNAS 99:Suppl. 37280–87 [Google Scholar]
  8. Bruch EE, Mare RD. 8.  2006. Neighborhood choice and neighborhood change. Am. J. Sociol. 112:3667–709 [Google Scholar]
  9. Butts JA, Roman CG, Bostwick L, Porter JR. 9.  2015. Cure violence: a public health model to reduce gun violence. Annu. Rev. Public Health 36:39–53 [Google Scholar]
  10. Cerdá M, Tracy M, Ahern J, Galea S. 10.  2014. Addressing population health and health inequalities: the role of fundamental causes. Am. J. Public Health 104:Suppl. 4S609–19 [Google Scholar]
  11. Cerdá M, Tracy M, Keyes KM. 11.  2018. Reducing urban violence: a contrast of public health and criminal justice approaches. Epidemiology 29:1142–50 [Google Scholar]
  12. Cerdá M, Tracy M, Keyes KM, Galea S. 12.  2015. To treat or to prevent? Reducing the population burden of violence-related post-traumatic stress disorder. Epidemiology 26:5681–89 [Google Scholar]
  13. Chao D, Hashimoto H, Kondo N. 13.  2015. Dynamic impact of social stratification and social influence on smoking prevalence by gender: an agent-based model. Soc. Sci. Med. 147:280–87 [Google Scholar]
  14. Cherng ST, Tam J, Christine PJ, Meza R. 14.  2016. Modeling the effects of e-cigarettes on smoking behavior: implications for future adult smoking prevalence. Epidemiology 27:6819–26 [Google Scholar]
  15. Chowell G, Sattenspiel L, Bansal S, Viboud C. 15.  2016. Mathematical models to characterize early epidemic growth: a review. Phys. Life Rev. 18:66–97 [Google Scholar]
  16. Christakis NA, Fowler JH. 16.  2007. The spread of obesity in a large social network over 32 years. N. Engl. J. Med. 357:4370–79 [Google Scholar]
  17. Codella J, Safdar N, Heffernan R, Alagoz O. 17.  2015. An agent-based simulation model for Clostridium difficile infection control. Med. Decis. Mak. 35:2211–29 [Google Scholar]
  18. 18. Comm. Assess. Agent-Based Model. Inf. Tob. Prod. Regul., Board Popul. Health Public Health Pract., Inst. Med. 2015. Assessing the Use of Agent-Based Models for Tobacco Regulation Washington, DC: Natl. Acad. Press
  19. Cooley P, Lee BY, Brown S, Cajka J, Chasteen B. 19.  et al. 2010. Protecting health care workers: a pandemic simulation based on Allegheny County. Influenza Other Respir. Viruses 4:261–72 [Google Scholar]
  20. Cooley P, Solano E. 20.  2011. Agent-based model (ABM) validation considerations. Proc. SIMUL 2011: Third Int. Conf. Adv. Syst. Simul., Barcelona, Spain, Oct. 23–29126–31
  21. Crooks AT. 21.  2010. Constructing and implementing an agent-based model of residential segregation through vector GIS. Int. J. Geogr. Inf. Sci. 24:5661–75 [Google Scholar]
  22. Day TE, Ravi N, Xian H, Brugh A. 22.  2013. An agent-based modeling template for a cohort of veterans with diabetic retinopathy. PLOS ONE 8:6e66812 [Google Scholar]
  23. Day TE, Ravi N, Xian H, Brugh A. 23.  2014. Sensitivity of diabetic retinopathy associated vision loss to screening interval in an agent-based/discrete event simulation model. Comput. Biol. Med. 47:7–12 [Google Scholar]
  24. Diez Roux AV. 24.  2011. Complex systems thinking and current impasses in health disparities research. Am. J. Public Health 101:91627–34 [Google Scholar]
  25. Diez Roux AV. 25.  2015. Health in cities: Is a systems approach needed?. Cad. Saude Publica 31:Suppl. 19–13 [Google Scholar]
  26. 26. Diez Roux AV. 2015. Invited commentary: the virtual epidemiologist—promise and peril. Am. J. Epidemiol. 181:2100–2 [Google Scholar]
  27. El-Sayed AM, Scarborough P, Seemann L, Galea S. 27.  2012. Social network analysis and agent-based modeling in social epidemiology. Epidemiol. Perspect. Innov. 9:11 [Google Scholar]
  28. Enanoria WTA, Liu F, Zipprich J, Harriman K, Ackley S. 28.  et al. 2016. The effect of contact investigations and public health interventions in the control and prevention of measles transmission: a simulation study. PLOS ONE 11:12e0167160 [Google Scholar]
  29. Epstein JM. 29.  1999. Agent-based computational models and generative social science. Complexity 4:541–60 [Google Scholar]
  30. Epstein JM. 30.  2002. Modeling civil violence: an agent-based computational approach. PNAS 99:Suppl. 37243–50 [Google Scholar]
  31. Epstein JM. 31.  2009. Modelling to contain pandemics. Nature 460:7256687 [Google Scholar]
  32. Epstein JM, Axtell RL. 32.  1996. Growing Artificial Societies: Social Science from the Bottom Up Washington, DC: Brookings Inst. Press
  33. Epstein JM, Cummings DAT, Chakravarty S, Singha RM, Burke DS. 33. , eds. 2004. Toward a Containment Strategy for Smallpox Bioterror: An Individual-Based Computational Approach Washington, DC: Brook. Inst. Press
  34. Epstein JM, Pankajakshan R, Hammond RA. 34.  2011. Combining computational fluid dynamics and agent-based modeling: a new approach to evacuation planning. PLOS ONE 6:5e20139 [Google Scholar]
  35. Escudero DJ, Lurie MN, Mayer KH, Weinreb C, King M. 35.  et al. 2016. Acute HIV infection transmission among people who inject drugs in a mature epidemic setting. AIDS 30:162537–44 [Google Scholar]
  36. Fagiolo G, Moneta A, Windrum P. 36.  2007. A critical guide to empirical validation of agent-based models in economics: methodologies, procedures, and open problems. Comput. Econ. 30:3195–226 [Google Scholar]
  37. Ferencik R, Minyard K. 37.  2011. Systems thinking in injury prevention: an innovative model for informing state and local policies. West. J. Emerg. Med. 12:3273–74 [Google Scholar]
  38. Ferguson NM, Cummings DAT, Cauchemez S, Fraser C, Riley S. 38.  et al. 2005. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437:7056209–14 [Google Scholar]
  39. Fink DS, Keyes KM, Cerdá M. 39.  2016. Social determinants of population health: a systems sciences approach. Curr. Epidemiol. Rep. 3:198–105 [Google Scholar]
  40. Fonoberova M, Fonoberov VA, Mezić I. 40.  2013. Global sensitivity/uncertainty analysis for agent-based models. Reliab. Eng. Syst. Saf. 118:8–17 [Google Scholar]
  41. Galea S, Riddle M, Kaplan GA. 41.  2010. Causal thinking and complex system approaches in epidemiology. Int. J. Epidemiol. 39:197–106 [Google Scholar]
  42. Gilbert N. 42.  2007. Agent-Based Models Los Angeles: SAGE. , 1st ed..
  43. Gorman DM, Mezic J, Mezic I, Gruenewald PJ. 43.  2006. Agent-based modeling of drinking behavior: a preliminary model and potential applications to theory and practice. Am. J. Public Health 96:112055–60 [Google Scholar]
  44. 44. Gov. Off. Sci. 2007. Reducing obesity: obesity system map Gov. Off. Sci London: https://www.gov.uk/government/publications/reducing-obesity-obesity-system-map
  45. Grefenstette JJ, Brown ST, Rosenfeld R, DePasse J, Stone NTB. 45.  et al. 2013. FRED (a Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations. BMC Public Health 13:940 [Google Scholar]
  46. Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V. 46.  et al. 2006. A standard protocol for describing individual-based and agent-based models. Ecol. Model. 198:115–26 [Google Scholar]
  47. Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF. 47.  2010. The ODD protocol: a review and first update. Ecol. Model. 221:2760–68 [Google Scholar]
  48. Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM. 48.  et al. 2005. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310:5750987–91 [Google Scholar]
  49. Gruenewald PJ. 49.  2007. The spatial ecology of alcohol problems: niche theory and assortative drinking. Addiction 102:6870–78 [Google Scholar]
  50. Guclu H, Kumar S, Galloway D, Krauland M, Sood R. 50.  et al. 2016. An agent-based model for addressing the impact of a disaster on access to primary care services. Disaster Med. Public Health Prep. 10:3386–93 [Google Scholar]
  51. Halloran ME, Longini IM, Nizam A, Yang Y. 51.  2002. Containing bioterrorist smallpox. Science 298:55971428–32 [Google Scholar]
  52. Hammond RA, Ornstein JT. 52.  2014. A model of social influence on body mass index. Ann. N. Y. Acad. Sci. 1331:34–42 [Google Scholar]
  53. Hupert N, Xiong W, Mushlin A. 53.  2008. The virtue of virtuality: the promise of agent-based epidemic modeling. Transl. Res. J. Lab. Clin. Med. 151:6273–74 [Google Scholar]
  54. Ip EH, Rahmandad H, Shoham DA, Hammond R, Huang TT-K. 54.  et al. 2013. Reconciling statistical and systems science approaches to public health. Health Educ. Behav. 40:Suppl. 1S123–31 [Google Scholar]
  55. Kalton A, Falconer E, Docherty J, Alevras D, Brann D, Johnson K. 55.  2016. Multi-agent-based simulation of a complex ecosystem of mental health care. J. Med. Syst. 40:239 [Google Scholar]
  56. Koopman JS, Lynch JW. 56.  1999. Individual causal models and population system models in epidemiology. Am. J. Public Health 89:81170–74 [Google Scholar]
  57. Kumar S, Piper K, Galloway DD, Hadler JL, Grefenstette JJ. 57.  2015. Is population structure sufficient to generate area-level inequalities in influenza rates? An examination using agent-based models. BMC Public Health 15:947 [Google Scholar]
  58. Laskowski M, Demianyk BCP, Friesen MR, McLeod RD, Mukhi SN. 58.  2011. Improving agent based models and validation through data fusion. Online J. Public Health Inform. 3:2 https://doi.org/10.5210/ojphi.v3i2.3607 [Crossref] [Google Scholar]
  59. Lee BY, Brown ST, Korch GW, Cooley PC, Zimmerman RK. 59.  et al. 2010. A computer simulation of vaccine prioritization, allocation, and rationing during the 2009 H1N1 influenza pandemic. Vaccine 28:314875–79 [Google Scholar]
  60. Lemoine PD, Cordovez JM, Zambrano JM, Sarmiento OL, Meisel JD. 60.  et al. 2016. Using agent based modeling to assess the effect of increased Bus Rapid Transit system infrastructure on walking for transportation. Prev. Med. 88:39–45 [Google Scholar]
  61. Levy DT, Bauer JE, Lee H-R. 61.  2006. Simulation modeling and tobacco control: creating more robust public health policies. Am. J. Public Health 96:3494–98 [Google Scholar]
  62. Levy DT, Meza R, Zhang Y, Holford TR. 62.  2016. Gauging the effect of U.S. tobacco control policies from 1965 through 2014 using SimSmoke. Am. J. Prev. Med. 50:4535–42 [Google Scholar]
  63. Li Y, Zhang D, Pagán JA. 63.  2016. Social norms and the consumption of fruits and vegetables across New York City neighborhoods. J. Urban Health Bull. N. Y. Acad. Med. 93:2244–55 [Google Scholar]
  64. Ligmann-Zielinska A, Kramer DB, Spence Cheruvelil K, Soranno PA. 64.  2014. Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance. PLOS ONE 9:10e109779 [Google Scholar]
  65. Link BG, Phelan J. 65.  1995. Social conditions as fundamental causes of disease. J. Health Soc. Behav. 35:80–94 [Google Scholar]
  66. Lofgren E. 66.  2017. Systems dynamics models. Systems Science and Population Health AM El-Sayed, S Galea 77–85 New York: Oxford Univ. Press [Google Scholar]
  67. Luke DA, Stamatakis KA. 67.  2012. Systems science methods in public health: dynamics, networks, and agents. Annu. Rev. Public Health 33:357–76 [Google Scholar]
  68. Lum K, Swarup S, Eubank S, Hawdon J. 68.  2014. The contagious nature of imprisonment: an agent-based model to explain racial disparities in incarceration rates. J. R. Soc. Interface 11:9820140409 [Google Scholar]
  69. Lustick I. 69.  2000. Agent-based modelling of collective identity: testing constructivist theory. J. Artif. Soc. Soc. Simul. 3:11 [Google Scholar]
  70. Mabry PL, Bures RM. 70.  2014. Systems science for obesity-related research questions: an introduction to the theme issue. Am. J. Public Health 104:71157–59 [Google Scholar]
  71. Mabry PL, Milstein B, Abraido-Lanza AF, Livingood WC, Allegrante JP. 71.  2013. Opening a window on systems science research in health promotion and public health. Health Educ. Behav. 40:Suppl. 1S5–8 [Google Scholar]
  72. Macal CM, North MJ. 72.  2010. Tutorial on agent-based modelling and simulation. J. Simul. 4:3151–62 [Google Scholar]
  73. Magliocca NR, Brown DG, Ellis EC. 73.  2013. Exploring agricultural livelihood transitions with an agent-based virtual laboratory: global forces to local decision-making. PLOS ONE 8:9e73241 [Google Scholar]
  74. Marshall BDL, Friedman SR, Monteiro JFG, Paczkowski M, Tempalski B. 74.  et al. 2014. Prevention and treatment produced large decreases in HIV incidence in a model of people who inject drugs. Health Aff. (Millwood) 33:3401–9 [Google Scholar]
  75. Marshall BDL, Galea S. 75.  2015. Formalizing the role of agent-based modeling in causal inference and epidemiology. Am. J. Epidemiol. 181:292–99 [Google Scholar]
  76. Marshall BDL, Paczkowski MM, Seemann L, Tempalski B, Pouget ER. 76.  et al. 2012. A complex systems approach to evaluate HIV prevention in metropolitan areas: preliminary implications for combination intervention strategies. PLOS ONE 7:9e44833 [Google Scholar]
  77. McClure RJ, Adriazola-Steil C, Mulvihill C, Fitzharris M, Salmon P. 77.  et al. 2015. Simulating the dynamic effect of land use and transport policies on the health of populations. Am. J. Public Health 105:Suppl. 2S223–29 [Google Scholar]
  78. Metcalf SS, Northridge ME, Widener MJ, Chakraborty B, Marshall SE, Lamster IB. 78.  2013. Modeling social dimensions of oral health among older adults in urban environments. Health Educ. Behav. 40:Suppl. 1S63–73 [Google Scholar]
  79. Mitchell M. 79.  2011. Complexity: A Guided Tour Oxford, UK: Oxford Univ. Press. , 1st ed..
  80. Moss S. 80.  2008. Alternative approaches to the empirical validation of agent-based models. J. Artif. Soc. Soc. Simul. 11:15 [Google Scholar]
  81. Murray EJ, Robins JM, Seage GR, Freedberg KA, Hernan MA. 81.  2017. A comparison of agent-based models and the parametric g-formula for causal inference. Am. J. Epidemiol. 186:2131–42 [Google Scholar]
  82. Murray M. 82.  2002. Determinants of cluster distribution in the molecular epidemiology of tuberculosis. PNAS 99:31538–43 [Google Scholar]
  83. Ness RB, Koopman JS, Roberts MS. 83.  2007. Causal system modeling in chronic disease epidemiology: a proposal. Ann. Epidemiol. 17:7564–68 [Google Scholar]
  84. Nianogo RA, Arah OA. 84.  2015. Agent-based modeling of noncommunicable diseases: a systematic review. Am. J. Public Health 105:3e20–31 [Google Scholar]
  85. Northridge ME, Metcalf SS. 85.  2016. Enhancing implementation science by applying best principles of systems science. Health Res. Policy Syst. 14:174 [Google Scholar]
  86. Orr MG, Kaplan GA, Galea S. 86.  2016. Neighbourhood food, physical activity, and educational environments and black/white disparities in obesity: a complex systems simulation analysis. J. Epidemiol. Community Health 70:9862–67 [Google Scholar]
  87. Parker J, Epstein JM. 87.  2011. A distributed platform for global-scale agent-based models of disease transmission. ACM Trans. Model. Comput. Simul. 22:12 [Google Scholar]
  88. Railsback S, Grimm V. 88.  2012. Agent-Based and Individual-Based Modeling: A Practical Introduction Princeton, NJ: Princeton Univ. Press
  89. Resnicow K, Page SE. 89.  2008. Embracing chaos and complexity: a quantum change for public health. Am. J. Public Health 98:81382–89 [Google Scholar]
  90. Schaefer DR, Adams J, Haas SA. 90.  2013. Social networks and smoking: exploring the effects of peer influence and smoker popularity through simulations. Health Educ. Behav. 40:Suppl. 1S24–32 [Google Scholar]
  91. Schelling TC. 91.  1971. Dynamic models of segregation. J. Math. Sociol. 1:2143–86 [Google Scholar]
  92. Schelling TC. 92.  2006. Micromotives and Macrobehavior New York: W.W. Norton & Co. Rev. ed.
  93. Scott N, Hart A, Wilson J, Livingston M, Moore D, Dietze P. 93.  2016. The effects of extended public transport operating hours and venue lockout policies on drinking-related harms in Melbourne, Australia: results from SimDrink, an agent-based simulation model. Int. J. Drug Policy 32:44–49 [Google Scholar]
  94. Siettos CI, Russo L. 94.  2013. Mathematical modeling of infectious disease dynamics. Virulence 4:4295–306 [Google Scholar]
  95. Speybroeck N, Van Malderen C Harper S, Müller B, Devleesschauwer B. 95.  2013. Simulation models for socioeconomic inequalities in health: a systematic review. Int. J. Environ. Res. Public Health 10:115750–80 [Google Scholar]
  96. Sterman JD. 96.  2006. Learning from evidence in a complex world. Am. J. Public Health 96:3505–14 [Google Scholar]
  97. Tracy M. 97.  2017. Systems approaches to understanding how the environment influences population health and population health interventions. Systems Science and Population Health AM El-Sayed, S Galea 151–65 New York: Oxford Univ. Press [Google Scholar]
  98. Tracy M, Braga AA, Papachristos AV. 98.  2016. The transmission of gun and other weapon-involved violence within social networks. Epidemiol. Rev. 38:170–86 [Google Scholar]
  99. Tracy M, Cerdá M, Galea S. 99.  2012. Causal thinking and complex systems approaches for understanding the consequences of trauma. Trauma, Psychopathology, and Violence: Causes, Consequences, or Correlates? C Widom 233–64 New York: Oxford Univ. Press [Google Scholar]
  100. Trochim WM, Cabrera DA, Milstein B, Gallagher RS, Leischow SJ. 100.  2006. Practical challenges of systems thinking and modeling in public health. Am. J. Public Health 96:3538–46 [Google Scholar]
  101. 101. Univ. Pittsbg. MIDAS Natl. Cent. Excell. 2017. Models of infectious disease agent study Univ. Pittsburgh, Pittsburgh, Pa https://www.midas.pitt.edu/
  102. Verella JT, Patek SD. 102.  2009. Toward an agent-based patient-physician model for the adoption of continuous glucose monitoring technology. J. Diabetes Sci. Technol. 3:2353–62 [Google Scholar]
  103. Westreich D. 103.  2017. From patients to policy: population intervention effects in epidemiology. Epidemiology 28:4525–28 [Google Scholar]
  104. Wheaton WD, RTI Int. 104.  2014. U.S. Synthetic Population 2010 Version 1.0: Quick Start Guide. Research Triangle Park, NC: RTI Int http://www.epimodels.org/10_Midas_Docs/SynthPop/2010_synth_pop_ver1_quickstart.pdf
  105. Windrum P, Fagiolo G, Moneta A. 105.  Empirical validation of agent-based models: alternatives and prospects. J. Artif. Soc. Soc. Simul. 10:28 [Google Scholar]
  106. Yang Y, Diez-Roux AV. 106.  2013. Using an agent-based model to simulate children's active travel to school. Int. J. Behav. Nutr. Phys. Act. 10:67 [Google Scholar]
  107. Yang Y, Diez Roux AV, Auchincloss AH, Rodriguez DA, Brown DG. 107.  2011. A spatial agent-based model for the simulation of adults’ daily walking within a city. Am. J. Prev. Med. 40:3353–61 [Google Scholar]
  108. Yang Y, Diez Roux AV, Auchincloss AH, Rodriguez DA, Brown DG. 108.  2012. Exploring walking differences by socioeconomic status using a spatial agent-based model. Health Place 18:196–99 [Google Scholar]
  109. Yang Y, Diez-Roux AV, Evenson KR, Colabianchi N. 109.  2014. Examining the impact of the walking school bus with an agent-based model. Am. J. Public Health 104:71196–203 [Google Scholar]
  110. Yonas MA, Burke JG, Brown ST, Borrebach JD, Garland R. 110.  et al. 2013. Dynamic simulation of crime perpetration and reporting to examine community intervention strategies. Health Educ. Behav. 40:Suppl. 1S87–97 [Google Scholar]
/content/journals/10.1146/annurev-publhealth-040617-014317
Loading
/content/journals/10.1146/annurev-publhealth-040617-014317
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error