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Related Experiment Video

Updated: Feb 8, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least

Alexandra Smirnova¹, Gerardo Chowell^2,3

¹Department of Mathematics and Statistics, Georgia State University, Atlanta, USA.

Infectious Disease Modelling

|June 22, 2018

Summary

This summary is machine-generated.

This study uses regularization methods with the generalized Richards model for disease forecasting. It helps public health officials predict epidemic outbreaks like the 2014-15 Ebola epidemic.

Keywords:

Ebola Epidemic forecasting Generalized Richards model Parameter estimation Regularization methods

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Last Updated: Feb 8, 2026

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Area of Science:

Epidemiology
Mathematical Modeling
Public Health

Background:

Effective disease forecasting systems are crucial for managing epidemic and pandemic outbreaks.
Phenomenological models offer a way to characterize epidemic growth without detailed transmission data.
The 2014-15 Ebola epidemic in West Africa highlights the need for robust forecasting tools.

Purpose of the Study:

To discuss and illustrate the application of regularization methods for parameter estimation.
To demonstrate the use of these methods for generating disease forecasts.
To apply these techniques within the context of the 2014-15 Ebola epidemic.

Main Methods:

Utilized the generalized Richards model, a phenomenological approach.
Employed regularization methods for parameter estimation in the model.
Applied the model and methods to data from the 2014-15 Ebola epidemic.

Main Results:

Regularization methods effectively estimated parameters for the generalized Richards model.
The model successfully generated short-term forecasts for the Ebola epidemic.
Demonstrated the utility of phenomenological models in disease outbreak scenarios.

Conclusions:

Regularization techniques enhance the reliability of disease forecasting models.
Phenomenological models, like the generalized Richards model, are valuable tools for public health preparedness.
Accurate forecasting aids in timely responses to infectious disease threats.