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Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

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Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using

Gerardo Chowell¹, Amanda Bleichrodt¹, Ruiyan Luo¹

¹Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, USA.

Statistics in Medicine

|February 20, 2024

Summary

This summary is machine-generated.

This study introduces QuantDiffForecast, a MATLAB toolbox for estimating parameters and forecasting time-series data from ordinary differential equation (ODE) models. It quantifies uncertainty in predictions, aiding scientific hypothesis assessment and system state forecasting.

Keywords:

ODE model toolbox ordinary differential equations parameter estimation real‐time forecasting and performance tutorial

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Area of Science:

Mathematical modeling
Dynamical systems analysis
Computational biology

Background:

Ordinary differential equations (ODEs) are crucial for modeling complex systems in science.
Accurate parameter estimation and prediction with quantified uncertainty are essential for ODE model application.
Existing tools may lack comprehensive functionality for parameter estimation and forecasting from ODEs.

Purpose of the Study:

To present QuantDiffForecast, a flexible MATLAB toolbox for parameter estimation and forecasting using ODE models.
To provide a user-friendly resource with detailed guidance and software for uncertainty quantification in predictions.
To support diverse users, including students, in applying ODE models for scientific inquiry.

Main Methods:

Development of a MATLAB toolbox (QuantDiffForecast) for ODE model analysis.
Implementation of parametric bootstrapping for uncertainty quantification in forecasts.
Application of the toolbox to epidemic models using 1918 influenza pandemic data.

Main Results:

The QuantDiffForecast toolbox enables robust parameter estimation and short-term forecasting from ODE models.
The software quantifies prediction uncertainty, enhancing model reliability.
Tutorials and example applications demonstrate the toolbox's utility and ease of use.

Conclusions:

QuantDiffForecast offers a comprehensive solution for parameter estimation and forecasting with ODEs.
The toolbox facilitates the assessment of hypotheses and prediction of system states with quantified uncertainty.
This resource is broadly applicable across scientific disciplines utilizing dynamical systems modeling.