CE-ABC: Cross-Entropy Approximate Bayesian Computation is a Matlab package that implements a framework for uncertainty quantification in mechanistic epidemic models defined by ordinary differential equations (ODEs). This package combines the cross-entropy method for optimization and approximate Bayesian computation for statistical inference. With straightforward adaptations, the CE-ABC strategy can be applied to various other systems, including mechanical, electrical, and coupled systems.
- Overview
- Features
- Usage
- Documentation
- Reproducibility
- Authors
- Citing CE-ABC
- License
- Institutional support
- Funding
CE-ABC addresses model calibration and uncertainty quantification in mechanistic models, primarily for epidemic modeling. The package integrates the cross-entropy method, which is a powerful optimization technique, with approximate Bayesian computation, a statistical inference method. This combination allows for efficient and accurate calibration and uncertainty quantification in ODE-based models.
For more details, refer to the following paper:
- A. Cunha Jr, D. A. W. Barton, and T. G. Ritto, Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation, Nonlinear Dynamics, vol. 111, pp. 9649–9679, 2023. DOI
Preprint available here.
- Combines cross-entropy method for optimization with approximate Bayesian computation for statistical inference
- Applicable to mechanistic models defined by ODEs
- Flexible framework for various systems (mechanical, electrical, coupled, etc.)
- Numerically robust and efficient implementation
- Educational style for intuitive use
- Includes example scripts for representative benchmark tests
To get started with CE-ABC, follow these steps:
- Clone the repository:
git clone https://github.com/americocunhajr/CE-ABC.git
- Navigate to the code directory:
cd CE-ABC/CE-ABC-1.0
- For a deterministic simulation with SEIRpAHD model, execute:
Main_IVP_SEIRpAHD
- For a stochastic simulation with SEIRpAHD model, execute:
Main_CE_ABC_SEIRpAHD
- For a stochastic simulation with SEIRpAHDbeta model, execute:
Main_CE_ABC_SEIRpAHDbeta
- To plot Rio de Janeiro COVID-19 data, execute:
Main_COVID19RJ_Data_plot
CE-ABC routines are well-commented to explain their functionality. Each routine includes a description of its purpose and a list of inputs and outputs. Examples with representative benchmark tests are provided to illustrate the code's functionality.
Simulations done with CE-ABC are fully reproducible, as can be seen on this CodeOcean capsule.
- Americo Cunha Jr
- David A. W. Barton
- Thiago G. Ritto
If you use CE-ABC in your research, please cite the following publication:
- A. Cunha Jr, D. A. W. Barton, and T. G. Ritto, Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation, Nonlinear Dynamics, vol. 111, pp. 9649–9679, 2023 https://doi.org/10.1007/s11071-023-08327-8
@article{CunhaJr2023p9649,
author = {A {Cunha~Jr} and D. A. W. Barton and T. G. Ritto},
title = {Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation},
journal = {Nonlinear Dynamics},
year = {2023},
volume = {111},
pages = {9649–9679},
doi = {10.1007/s11071-023-08327-8},
}
CE-ABC is released under the MIT license. See the LICENSE file for details. All new contributions must be made under the MIT license.