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一分钟完成验证Calibration tools for agent-based epidemiological models built with epiworldR
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取自仓库 README
正在加载完整 README。 以下为已保存的摘录。
How to cite epiworldRcalibrate. If you use epiworldRcalibrate in published work, please cite it:
Najafzadehkhoei S, Vega Yon GG, Modenesi B, Meyer D (2025). Machine Generalize Learning in Agent-Based Models. arXiv:2509.07013. doi:10.48550/arXiv.2509.07013
Run citation("epiworldRcalibrate") in R for the BibTeX entry.
epiworldRcalibrate: Fast and Effortless Calibration of Agent-Based Models using Machine Learning查看完整 README ↗[!NOTE] How to cite epiworldRcalibrate. If you use epiworldRcalibrate in published work, please cite it:
Najafzadehkhoei S, Vega Yon GG, Modenesi B, Meyer D (2025). Machine Generalize Learning in Agent-Based Models. arXiv:2509.07013. doi:10.48550/arXiv.2509.07013
Run
citation("epiworldRcalibrate")in R for the BibTeX entry.
From the DESCRIPTION:
The 'epiworldRcalibrate' package provides tools and pre-trained Machine Learning [ML] models for calibration of Agent-Based Models [ABMs] built with the R package 'epiworldR'. It implements methods described in Najafzadehkhoei, Vega Yon, Modenesi, and Meyer (2025) doi:10.48550/arXiv.2509.07013. Using 'epiworldRcalibrate', users can automatically calibrate ABMs in seconds with its pre-trained ML models, effectively focusing on simulation rather than calibration. This tool bridges a gap by allowing public health practitioners to run their own ABMs without the advanced technical expertise often required by calibration.
epiworldRcalibrate provides fast, data-driven calibration of SIR epidemic parameters using a pretrained Bidirectional LSTM (BiLSTM) model. Given a single incidence time series, the package estimates:
ptran)crate)R0)The package is fully integrated with epiworldR and requires no external Python setup.
calibrate_sir()# Install from GitHub
devtools::install_github("sima-njf/epiworldRcalibrate")
library(epiworldR)
library(epiworldRcalibrate)
# simulate SIR model
m <- ModelSIRCONN("sim", n=8000, prevalence=0.01,
contact_rate=3, transmission_rate=0.25,
recovery_rate=0.1)
run(m, ndays = 60)
inc <- plot_incidence(m)[,1]
# one-line calibration
calibrate_sir(
daily_cases = inc,
population_size = 8000,
recovery_rate = 0.1
)
Use epiworldRcalibrate when you want to:
Full website, reference, and vignette: 👉 https://sima-njf.github.io/epiworldRcalibrate/
Developed by Sima Najafzadehkhoei 🔗 https://github.com/sima-njf