Computational Model Library

Kenya ITN Agent-Based Microsimulation (2003–2024) (1.0.0)

An agent-based microsimulation of insecticide-treated net (ITN) distribution and adoption in Kenya (2003–2024), integrating the Theory of Planned Behaviour, Rogers diffusion, Weibull net decay, and a GPS-based two-layer social network. 8,561 household agents calibrated via Approximate Bayesian Computation to six DHS/MIS survey waves, achieving 2.42 pp mean absolute error on Kenya-level ownership. The analysis chain supports mechanism counterfactuals and policy experiments on equity outcomes of ITN distribution strategies.

Release Notes

First public release associated with the JASSS submission (Kim & Shin, 2026, under review).

Contents:
- Core agent-based microsimulation (21_itn_abm_v5.py): 8,561 household agents, monthly time steps 2003-2024
- Calibration pipeline (ABC v7, 4 free parameters, 1,000 LHS candidates → 200 accepted, best MAE 2.42 pp on six DHS/MIS survey waves)
- Sensitivity analyses: PRCC (N=200) and Sobol’ global sensitivity (N=128, 6 parameters)
- Ensemble experiments: 9 main-text scenarios × 20 seeds + P3b budget-matched cohort rotation + S4 pro-poor targeting removal
- Time-series experiments: main + v9 Fig 10 extras (S4/P3a/P3b trajectories, 20 seeds each)
- Robustness checks: S1 social-norm weight configurations and A3-A6 mechanism ablations
- All reproducible checkpoints (JSON)
- Full ODD protocol (Grimm et al. 2020)
- DHS preprocessing pipeline

Reproduces all figures and tables in the manuscript.

Associated Publications

Kim, W., & Shin, H. (2026). Nonlinear equity effects of ITN distribution policies in Kenya: An agent-based microsimulation with empirical calibration and ensemble analysis. Journal of Artificial Societies and Social Simulation. [Manuscript under review]

Kenya ITN Agent-Based Microsimulation (2003–2024) 1.0.0

Submitted by Wooyoung Kim Published Apr 18, 2026 Last modified Apr 18, 2026
agent-based microsimulation insecticide-treated nets Malaria kenya ODD protocol pattern-oriented modelling DHS

An agent-based microsimulation of insecticide-treated net (ITN) distribution and adoption in Kenya (2003–2024), integrating the Theory of Planned Behaviour, Rogers diffusion, Weibull net decay, and a GPS-based two-layer social network. 8,561 household agents calibrated via Approximate Bayesian Computation to six DHS/MIS survey waves, achieving 2.42 pp mean absolute error on Kenya-level ownership. The analysis chain supports mechanism counterfactuals and policy experiments on equity outcomes of ITN distribution strategies.

Release Notes

First public release associated with the JASSS submission (Kim & Shin, 2026, under review).

Contents:
- Core agent-based microsimulation (21_itn_abm_v5.py): 8,561 household agents, monthly time steps 2003-2024
- Calibration pipeline (ABC v7, 4 free parameters, 1,000 LHS candidates → 200 accepted, best MAE 2.42 pp on six DHS/MIS survey waves)
- Sensitivity analyses: PRCC (N=200) and Sobol’ global sensitivity (N=128, 6 parameters)
- Ensemble experiments: 9 main-text scenarios × 20 seeds + P3b budget-matched cohort rotation + S4 pro-poor targeting removal
- Time-series experiments: main + v9 Fig 10 extras (S4/P3a/P3b trajectories, 20 seeds each)
- Robustness checks: S1 social-norm weight configurations and A3-A6 mechanism ablations
- All reproducible checkpoints (JSON)
- Full ODD protocol (Grimm et al. 2020)
- DHS preprocessing pipeline

Reproduces all figures and tables in the manuscript.

Cite this Model

Wooyoung Kim, Hosang Shin (2026, April 18). “Kenya ITN Agent-Based Microsimulation (2003–2024)” (Version 1.0.0). CoMSES Computational Model Library. Retrieved from: https://www.comses.net/codebases/002e0b87-48ae-4815-a68c-83403baea4d1/releases/1.0.0/

Associated Publication(s)

Kim, W., & Shin, H. (2026). Nonlinear equity effects of ITN distribution policies in Kenya: An agent-based microsimulation with empirical calibration and ensemble analysis. Journal of Artificial Societies and Social Simulation. [Manuscript under review]

References

Grimm, V., Railsback, S. F., Vincenot, C. E., Berger, U., Gallagher, C., DeAngelis, D. L., Edmonds, B., Ge, J., Giske, J., Groeneveld, J., Johnston, A. S. A., Milles, A., Nabe-Nielsen, J., Polhill, J. G., Radchuk, V., Rohwäder, M.-S., Stillman, R. A., Thiele, J. C., & Ayllón, D. (2020). The ODD protocol for describing agent-based and other simulation models: A second update to improve clarity, replication, and structural realism. Journal of Artificial Societies and Social Simulation, 23(2), 7. https://doi.org/10.18564/jasss.4259

Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., Thulke, H.-H., Weiner, J., Wiegand, T., & DeAngelis, D. L. (2005). Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310(5750), 987-991. https://doi.org/10.1126/science.1116681

ten Broeke, G., van Voorn, G., & Ligtenberg, A. (2016). Which sensitivity analysis method should I use for my agent-based model? Journal of Artificial Societies and Social Simulation, 19(1), 5. https://doi.org/10.18564/jasss.2857

Thiele, J. C., Kurth, W., & Grimm, V. (2014). Facilitating parameter estimation and sensitivity analysis of agent-based models: A cookbook using NetLogo and R. Journal of Artificial Societies and Social Simulation, 17(3), 11. https://doi.org/10.18564/jasss.2503

Collins, A., Koehler, M., & Lynch, C. (2024). Methods that support the validation of agent-based models: An overview and discussion. Journal of Artificial Societies and Social Simulation, 27(1), 11. https://doi.org/10.18564/jasss.5258

Richiardi, M. G. (2014). The missing link: AB models and dynamic microsimulation. In S. Leitner & F. Wall (Eds.), Artificial Economics and Self Organization (pp. 3-15). Springer. https://doi.org/10.1007/978-3-319-00912-4_1

Tracy, M., Cerdá, M., & Keyes, K. M. (2018). Agent-based modeling in public health: Current applications and future directions. Annual Review of Public Health, 39, 77-94. https://doi.org/10.1146/annurev-publhealth-040617-014317

Bertozzi-Villa, A., Bever, C. A., Koenker, H., Weiss, D. J., Vargas-Ruiz, C., Nandi, A. K., Gibson, H. S., Harris, J., Battle, K. E., Rumisha, S. F., Keddie, S., Amratia, P., Arambepola, R., Cameron, E., Chestnutt, E. G., Collins, E. L., Millar, J., Mishra, S., Rozier, J., . . . Bhatt, S. (2021). Maps and metrics of insecticide-treated net access, use, and nets-per-capita in Africa from 2000-2020. Nature Communications, 12, 3589. https://doi.org/10.1038/s41467-021-23707-7

Smith, N. R., Trauer, J. M., Gambhir, M., Richards, J. S., Maude, R. J., Keith, J. M., & Flegg, J. A. (2018). Agent-based models of malaria transmission: A systematic review. Malaria Journal, 17, 299. https://doi.org/10.1186/s12936-018-2442-y

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T

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Contributors
Wooyoung Kim Hosang Shin
DOI
No assigned DOI
Model Version
1.0.0
License
MIT
Programming Language
Python 3.9+
Software Framework
numpy pandas scipy matplotlib salib
Operating System
Operating System Independent
Publish Date
Saturday, April 18, 2026
Last Updated
Saturday, April 18, 2026
Downloads 1
Peer Review
Unreviewed
Version Submitter First published Last modified Status
1.0.0 Wooyoung Kim Sat Apr 18 14:57:03 2026 Sat Apr 18 14:57:05 2026 Published

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