Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial
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Wiley
2024
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| _version_ | 1867005917891919872 |
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| author | Gerardo Chowell Amanda Bleichrodt Ruiyan Luo |
| author_facet | Gerardo Chowell Amanda Bleichrodt Ruiyan Luo Gerardo Chowell Amanda Bleichrodt Ruiyan Luo |
| collection | Wiley Open Access |
| contents | Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial Gerardo Chowell Amanda Bleichrodt Ruiyan Luo Statistics in Medicine Mathematical models based on systems of ordinary differential equations (ODEs) are frequently applied in various scientific fields to assess hypotheses, estimate key model parameters, and generate predictions about the system's state. To support their application, we present a comprehensive, easy‐to‐use, and flexible MATLAB toolbox, QuantDiffForecast, and associated tutorial to estimate parameters and generate short‐term forecasts with quantified uncertainty from dynamical models based on systems of ODEs. We provide software ( https://github.com/gchowell/paramEstimation_forecasting_ODEmodels/) and detailed guidance on estimating parameters and forecasting time‐series trajectories that are characterized using ODEs with quantified uncertainty through a parametric bootstrapping approach. It includes functions that allow the user to infer model parameters and assess forecasting performance for different ODE models specified by the user, using different estimation methods and error structures in the data. The tutorial is intended for a diverse audience, including students training in dynamic systems, and will be broadly applicable to estimate parameters and generate forecasts from models based on ODEs. The functions included in the toolbox are illustrated using epidemic models with varying levels of complexity applied to data from the 1918 influenza pandemic in San Francisco. A tutorial video that demonstrates the functionality of the toolbox is included. 10.1002/sim.10036 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| doi_str_mv | 10.1002/sim.10036 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_10036 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial Gerardo Chowell Amanda Bleichrodt Ruiyan Luo Statistics in Medicine Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial Gerardo Chowell Amanda Bleichrodt Ruiyan Luo Statistics in Medicine Mathematical models based on systems of ordinary differential equations (ODEs) are frequently applied in various scientific fields to assess hypotheses, estimate key model parameters, and generate predictions about the system's state. To support their application, we present a comprehensive, easy‐to‐use, and flexible MATLAB toolbox, QuantDiffForecast, and associated tutorial to estimate parameters and generate short‐term forecasts with quantified uncertainty from dynamical models based on systems of ODEs. We provide software ( https://github.com/gchowell/paramEstimation_forecasting_ODEmodels/) and detailed guidance on estimating parameters and forecasting time‐series trajectories that are characterized using ODEs with quantified uncertainty through a parametric bootstrapping approach. It includes functions that allow the user to infer model parameters and assess forecasting performance for different ODE models specified by the user, using different estimation methods and error structures in the data. The tutorial is intended for a diverse audience, including students training in dynamic systems, and will be broadly applicable to estimate parameters and generate forecasts from models based on ODEs. The functions included in the toolbox are illustrated using epidemic models with varying levels of complexity applied to data from the 1918 influenza pandemic in San Francisco. A tutorial video that demonstrates the functionality of the toolbox is included. 10.1002/sim.10036 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| title | Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.10036 |