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for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
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Thanks for the PR. Could you try to fix the pre-commit issues? |
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You have probably pushed two different models. |
Merge branch 'main' of https://github.com/BittuAgarwal1237/mesa-examples into add-seir-vaccination-example
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Fixed! Removed the hierarchical organization model from this branch -it belongs in a separate PR. This PR now contains only the SEIR model @codebreaker32 |
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Please remove it from README.md as well |
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done @codebreaker32 |
examples/seir_vaccination/agents.py
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| States: | ||
| S - Susceptible: healthy, can get infected | ||
| E - Exposed: infected but not yet contagious | ||
| I - Infected: contagious, can spread to neighbors | ||
| R - Recovered: immune, cannot be infected again |
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I think using a proper Enum would be better, ref https://github.com/mesa/mesa/blob/main/mesa/examples/advanced/epstein_civil_violence/agents.py
examples/seir_vaccination/app.py
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| def agent_portrayal(agent): | ||
| return { | ||
| "color": STATE_COLORS.get(agent.state, "grey"), |
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Using raw dict is deprecated, Use AgentPortrayalStyle
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Thanks for the suggestion! I looked into AgentPortrayalStyle but it doesn't seem to be available in the current Mesa dev version (4.0.0.dev0). Happy to update once it's added to the codebase, for now keeping the dict-based portrayal. Let me know if there's another approach you'd prefer!
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examples/seir_vaccination/model.py
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| def step(self): | ||
| # 1. All people act first | ||
| for person in self.population: |
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Can we use agents_by_type here?
| person.step() | ||
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| # 2. Government monitors and responds | ||
| self.government.step() |
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You should use agents_by_type and then can use shuffle_do for Random Activation
examples/seir_vaccination/README.md
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| ```bash | ||
| python -m venv venv | ||
| source venv/bin/activate # Windows: venv\Scripts\activate | ||
| pip install -r requirements.txt |
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There's no requirements.txt?
| ## Overview | ||
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| This model simulates how a disease spreads through a population on a grid, and how a government responds when things get bad enough. | ||
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could you cite the original paper as well?
| ### [Humanitarian Aid Distribution Model](https://github.com/mesa/mesa-examples/tree/main/examples/humanitarian_aid_distribution) | ||
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| This model simulates a humanitarian aid distribution scenario using a needs-based behavioral architecture. Beneficiaries have dynamic needs (water, food) and trucks distribute aid using a hybrid triage system. | ||
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Add your model here
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Updated all the feedback - used State Enum, switched to agents_by_type with shuffle_do, fixed README (removed requirements.txt, added paper citation, added model to main README). Model tested locally and running fine! |
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done the changes @codebreaker32 |
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Summary
Adds a SEIR epidemic model with a government vaccination policy meta-agent.
Motive
There's no population-level epidemic spreading example in mesa-examples. The existing
virus_antibodymodel works at the biological/cellular level, but nothing shows how a disease spreads through a population on a grid and how a policy intervention affects that. This fills that gap.Implementation
PersonAgents move through four states --Susceptible, Exposed, Infected, Recovered, based on contact with infected neighbors. A GovernmentAgent monitors the infection rate each step and triggers vaccination campaigns when it crosses a threshold, directly moving some susceptible people to recovered. Activation is explicit and bottom-up: people act first, then the government responds. No legacy schedulers used.
Usage Examples
Sliders let you adjust transmission rate, incubation period, infection duration, vaccination threshold, and campaign size. The grid shows agent states in color and two charts track SEIR curves and when vaccination campaigns are active.
@jackiekazil @tpike3 @EwoutH
