Corpus ID: 218684907

Inference, prediction and optimization of non-pharmaceutical interventions using compartment models: the PyRoss library

@article{Adhikari2020InferencePA,
  title={Inference, prediction and optimization of non-pharmaceutical interventions using compartment models: the PyRoss library},
  author={R. Adhikari and Austen Bolitho and F. Caballero and M. E. Cates and J. Dolezal and T. Ekeh and J. Guioth and R. L. Jack and J. Kappler and Lukas Kikuchi and H. Kobayashi and Y. Li and J. D. Peterson and Patrick Pietzonka and Benjamin Remez and P. Rohrbach and R. Singh and Gunther Turk},
  journal={arXiv: Populations and Evolution},
  year={2020}
}

R. Adhikari, Austen Bolitho, +15 authors Gunther Turk

Published 2020

Computer Science, Biology, Physics

arXiv: Populations and Evolution

PyRoss is an open-source Python library that offers an integrated platform for inference, prediction and optimisation of NPIs in age- and contact-structured epidemiological compartment models. This report outlines the rationale and functionality of the PyRoss library, with various illustrations and examples focusing on well-mixed, age-structured populations. The PyRoss library supports arbitrary structured models formulated stochastically (as master equations) or deterministically (as ODEs) and… CONTINUE READING

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