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BayesianFitForecast: a user-friendly R toolbox for parameter estimation and forecasting with ordinary differential
Hamed Karami1, Amanda Bleichrodt2, Ruiyan Luo2
1Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA.
BMC Medical Informatics and Decision Making
|October 15, 2025
Summary
BayesianFitForecast is a new R toolbox simplifying Bayesian parameter estimation and forecasting for ordinary differential equation (ODE) models. It lowers the coding barrier for complex dynamical systems, enhancing public health and epidemiological decision-making.
Area of Science:
- Computational Biology and Bioinformatics
- Epidemiology and Public Health
- Mathematical Modeling
Background:
- Ordinary differential equations (ODEs) are crucial for modeling dynamic systems in science and healthcare.
- Bayesian calibration and forecasting for ODE models often demand extensive coding expertise.
- A need exists for accessible tools to facilitate Bayesian inference in dynamical systems.
Purpose of the Study:
- Introduce BayesianFitForecast, a user-friendly R toolbox.
- Streamline Bayesian parameter estimation and forecasting for ODE models.
- Reduce the technical barrier for applying Bayesian methods in health informatics and public health.
Main Methods:
- Automated generation of Stan files for ODE models.
- User-friendly interface for model configuration and prior definition.
- Application to historical epidemic datasets (e.g., 1918 influenza, 1896 Bombay plague) and simulated data.
- Evaluation of parameter estimation and forecasting performance.
Main Results:
- Demonstrated robust parameter estimation and forecasting.
- Successful application to real-world and simulated epidemic data.
- Validated performance under different observation error structures (Poisson, negative binomial).
- Provided comprehensive model performance evaluation tools.
Conclusions:
- Enhanced accessibility of advanced Bayesian methods for time-series modeling and forecasting.
- Broadened applications in healthcare forecasting and epidemiological studies.
- Included an interactive Shiny web application and tutorial video for user support.
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