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, which combines the cross-entropy method for optimization and approximate Bayesian computation for statistical inference. With some straightforward adaptations, CE-ABC strategy can also be applied to other systems (mechanical, electrical, coupled, etc). More details are in 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 https://doi.org/10.1007/s11071-023-08327-8
Preprint available at: https://arxiv.org/abs/2207.12111
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
We ask the code users to cite the following manuscript in any publications reporting work done with our code:
- 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{CunhaJr2023p,
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.
