Aaby BG, Perumalla KS, Seal SK. Efficient simulation of agent-based models on multi-GPU and multi-core clusters. Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques. 2010. https://doi.org/10.4108/ICST.SIMUTOOLS2010.8822

Abar S, Theodoropoulos GK, Lemarinier P, O’Hare GMP. Agent Based Modelling and Simulation tools: a review of the state-of-art software. Comput Sci Rev. 2017;24:13–33. https://doi.org/10.1016/j.cosrev.2017.03.001

Article  Google Scholar 

Aumann C. A. A methodology for developing simulation models of complex systems. Ecol Model. 2007;202(3–4):385–96. https://doi.org/10.1016/j.ecolmodel.2006.11.005

Article  Google Scholar 

Bertelle C, Duchamp GHE, Kadri-Dahmani H, editors. Complex Systems and Self-organization modelling. Springer Berlin Heidelberg; 2009. https://doi.org/10.1007/978-3-540-88073-8

Broeck WVD, Gioannini C, Gonçalves B, Quaggiotto M, Colizza V, Vespignani A. The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect Dis. 2011;11(1):37. https://doi.org/10.1186/1471-2334-11-37

Article  PubMed  PubMed Central  Google Scholar 

Cooley P, Solano E. Agent-Based Model (ABM) Validation Considerations. SIMUL 2011: The Third International Conference on Advances in System Simulation, 2011;126–131.

Davis MC, Challenger R, Jayewardene DNW, Clegg CW. Advancing socio-technical systems thinking: a call for bravery. Appl Ergon. 2014;45(2):171–80. https://doi.org/10.1016/j.apergo.2013.02.009

Article  PubMed  Google Scholar 

Gilbert NA. Framework for Megascale Agent Based Model simulations on Graphics Processing Units. J Artif Soc Soc Simul, 2008;11(4).

Grefenstette JJ, Brown ST, Rosenfeld R, DePasse J, Stone NT, Cooley PC, Wheaton WD, Fyshe A, Galloway DD, Sriram A, Guclu H, Abraham T, Burke DS. 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. 2013;13(1):940. https://doi.org/10.1186/1471-2458-13-940

Article  PubMed  PubMed Central  Google Scholar 

Grimm V. Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future? Ecol Model. 1999;115(2–3):129–48. https://doi.org/10.1016/S0304-3800(98)00188-4

Article  Google Scholar 

Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, Goss-Custard J, Grand T, Heinz SK, Huse G, Huth A, Jepsen JU, Jørgensen C, Mooij WM, Müller B, Pe’er G, Piou C, Railsback SF, Robbins AM. DeAngelis, D. L. A standard protocol for describing individual-based and agent-based models. Ecol Model. 2006;198(1–2):115–26. https://doi.org/10.1016/j.ecolmodel.2006.04.023

Article  Google Scholar 

Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF. The ODD protocol: a review and first update. Ecol Model. 2010;221(23):2760–8. https://doi.org/10.1016/j.ecolmodel.2010.08.019

Article  Google Scholar 

Grimm V, Railsback SF, Vincenot CE, Berger U, Gallagher C, DeAngelis DL, Edmonds B, Ge J, Giske J, Groeneveld J, Johnston ASA, Milles A, Nabe-Nielsen J, Polhill JG, Radchuk V, Rohwäder M-S, Stillman RA, Thiele JC, Ayllón D. The ODD Protocol for describing Agent-based and other Simulation models: a second update to improve clarity, replication, and structural realism. J Artif Soc Soc Simul. 2020;23(2):7. https://doi.org/10.18564/jasss.4259. A recently introduced protocol for describing the structure and dynamics of ABMs. It advocates the need for standardization to make simulation experiments more accessible and comparable.

Article  Google Scholar 

Guo D, Li K, Peters T. Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it. Sci Rep. 2015;5:8980. https://doi.org/10.1038/srep08980

Article  CAS  PubMed  PubMed Central  Google Scholar 

Harbers M, Van Den Bosch K, Meyer J-J. Design and evaluation of explainable BDI agents. 2010 IEEE/WIC/ACM Int Conf Web Intell Intell Agent Technol. 2010;125–32. https://doi.org/10.1109/WI-IAT.2010.115

Karimi E, Schmitt K, Akgunduz A. Effect of individual protective behaviors on influenza transmission: an agent-based model. Health Care Manag Sci. 2015;18(3):318–33. https://doi.org/10.1007/s10729-014-9310-2

Article  PubMed  Google Scholar 

Larsen JB. Going beyond BDI for agent-based simulation. J Inform Telecommunication. 2019;3(4):446–64. https://doi.org/10.1080/24751839.2019.1620024

Article  Google Scholar 

Lawyer G. Measuring the potential of individual airports for pandemic spread over the world airline network. BMC Infectious Diseases, 2015;16(1):70. https://doi.org/10.1186/s12879-016-1350-4

Mitsopoulos, K, Bose K, Mather R, Bhatia B, Gluck A, Dorr K, Lebiere B, C., Pirolli P. Psychologically-Valid Generative Agents: A Novel Approach to Agent-Based Modeling in Social Sciences. Proceedings of the AAAI Symposium Series, 2024;2(1):340–348. https://doi.org/10.1609/aaaiss.v2i1.27698 A paper confirming the recent trend of using generative AI and LLMs (large language models) in the design process of models, aiming for more detailed simulations by incorporating highly-realistic human behaviour.

Moss S. Alternative approaches to the empirical validation of Agent-based models. J Artif Soc Soc Simul. 2008;11(1):5.

Google Scholar 

Ngonghala CN, Iboi E, Eikenberry S, Scotch M, MacIntyre CR, Bonds MH, Gumel AB. Mathematical assessment of the impact of non-pharmaceutical interventions on curtailing the 2019 novel coronavirus. Math Biosci. 2020;325108364. https://doi.org/10.1016/j.mbs.2020.108364

Pastore Y, Piontti A, Perra N, Rossi L, Samay N, Vespignani A. Charting the next pandemic: modeling infectious disease spreading in the Data Science Age. Springer International Publishing; 2019. https://doi.org/10.1007/978-3-319-93290-3

Perumalla KS, Aaby BG, Data Parallel Execution Challenges and Runtime Performance of Agent Simulations on GPUs, 2007.

*Pullano G, Valdano E, Scarpa N, Rubrichi S, Colizza V. Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: A population-based study. The Lancet Digital Health, 2020;2(12):e638–e649. https://doi.org/10.1016/S2589-7500(20)30243-0 A recent study assessing the effect of mobility reductions during lockdowns and identifying the factors driving the changes in social dynamics that affected viral diffusion.

Richmond P, Walker D, Coakley S, Romano D High performance cellular level agent-based simulation with FLAME for the GPU. Briefings in Bioinformatics, 2010;11(3):334–347. https://doi.org/10.1093/bib/bbp073

**Shamil MS, Farheen, F., Ibtehaz F, Khan N, I. M., Rahman MS. An Agent-Based Modeling of COVID-19: Validation, Analysis, and Recommendations. Cognitive Computation. 2021;25 A recently proposed ABM, using agents to represent individual humans highlighting the recent advances made in data generating possibilities and processing large-scale data, offering unprecedented insights.

Siettos C, Anastassopoulou C, Russo L, Grigoras C, Mylonakis E. Modeling the 2014 Ebola Virus Epidemic 2013 Agent-Based Simulations, Temporal Analysis and Future Predictions for Liberia and Sierra Leone. PLoS Currents. 2015. https://doi.org/10.1371/currents.outbreaks.8d5984114855fc425e699e1a18cdc6c9

*Thompson, J. A framework for considering the utility of models when facing tough decisions in public health: a guideline for policy-makers. Health Res Policy Sys, 2022;20:107. https://doi.org/10.1186/s12961-022-00902-6 A recent framework to support policy makers when adopting and utilising insights from public health, infectious disease and policy modelling when assessing the impact of public health measures.

Valle SYD, Mniszewski SM, Hyman JM. Modeling the Impact of Behavior Changes on the Spread of Pandemic Influenza. In P. Manfredi & A. D’Onofrio, editors, Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases Springer New York; 2013;59–77. https://doi.org/10.1007/978-1-4614-5474-8_4

Viboud C, Boëlle P-Y, Cauchemez S, Lavenu A, Valleron A-J, Flahault A, Carrat F. Risk factors of influenza transmission in households. Int Congr Ser. 2004;1263:291–4. https://doi.org/10.1016/j.ics.2004.01.013

Article  Google Scholar 

Villamor GB, Troitzsch KG, Vlek P. L. G. (n.d.). Human decision making for empirical agent-based models: Construction and validation.

Wijedasa S, Gunasekara C, Laskowsk M, Friesen MR, McLeod RD. Smartphone and vehicular trajectories as data sources for agent-based infection spread modelling. Health Syst. 2013;2(2):120–33. https://doi.org/10.1057/hs.2012.25

Article  Google Scholar 

Wooldridge M. An introduction to MultiAgent systems. 2nd ed. Incorporated: Wiley; 2009.

Google Scholar