https://github.com/americocunhajr/ce-abc

CE-ABC is a code to simulate the epidemic outbreaks with mechanistic models through a cross-entropy approximate Bayesian framework.
https://github.com/americocunhajr/ce-abc

approximate-bayesian-computation bayesian-inference compartmental-models computational-biology computational-epidemiology cross-entropy-method epidemic-simulations epidemiology mechanistic-models

Last synced: 12 months ago
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CE-ABC is a code to simulate the epidemic outbreaks with mechanistic models through a cross-entropy approximate Bayesian framework.

Host: GitHub
URL: https://github.com/americocunhajr/ce-abc
Owner: americocunhajr
License: mit
Created: 2022-01-06T01:22:18.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2025-04-04T20:09:30.000Z (about 1 year ago)
Last Synced: 2025-04-23T01:46:46.955Z (about 1 year ago)
Topics: approximate-bayesian-computation, bayesian-inference, compartmental-models, computational-biology, computational-epidemiology, cross-entropy-method, epidemic-simulations, epidemiology, mechanistic-models
Language: MATLAB
Homepage: https://americocunhajr.github.io/CE-ABC
Size: 68.1 MB
Stars: 7
Watchers: 2
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

## Cross-Entropy Approximate Bayesian Computation

**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.

### Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Usage](#usage)
- [Documentation](#documentation)
- [Reproducibility](#reproducibility)
- [Authors](#authors)
- [Citing CE-ABC](#citing-ce-abc)
- [License](#license)
- [Institutional support](#institutional-support)
- [Funding](#funding)

### Overview
**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.

### Features
- 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

### Usage
To get started with **CE-ABC**, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/americocunhajr/CE-ABC.git
```
2. Navigate to the code directory:
```bash
cd CE-ABC/CE-ABC-1.0
```
3. For a deterministic simulation with SEIRpAHD model, execute:
```bash
Main_IVP_SEIRpAHD
```
4. For a stochastic simulation with SEIRpAHD model, execute:
```bash
Main_CE_ABC_SEIRpAHD
```
5. For a stochastic simulation with SEIRpAHDbeta model, execute:
```bash
Main_CE_ABC_SEIRpAHDbeta
```
5. To plot Rio de Janeiro COVID-19 data, execute:
```bash
Main_COVID19RJ_Data_plot
```

### Documentation
**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.

### Reproducibility
Simulations done with **CE-ABC** are fully reproducible, as can be seen on this CodeOcean capsule.

### Authors
- Americo Cunha Jr
- David A. W. Barton
- Thiago G. Ritto

### Citing CE-ABC
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},
}
```

### License
**CE-ABC** is released under the MIT license. See the LICENSE file for details. All new contributions must be made under the MIT license.