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Скачать или смотреть Optimizing SIR Model Parameters in R with Time Varying Beta

  • vlogize
  • 2025-10-08
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Optimizing SIR Model Parameters in R with Time Varying Beta
In R: FME/ deSolve - SIR fitting (time varying parameters)parametersodedata fittingmodel fitting
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Описание к видео Optimizing SIR Model Parameters in R with Time Varying Beta

Discover how to effectively fit dynamic SIR model parameters using the FME and deSolve R packages by optimizing time-varying transmission rates.
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This video is based on the question https://stackoverflow.com/q/67478946/ asked by the user 'galaxy--' ( https://stackoverflow.com/u/14868525/ ) and on the answer https://stackoverflow.com/a/67490904/ provided by the user 'tpetzoldt' ( https://stackoverflow.com/u/3677576/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: In R: FME/ deSolve - SIR fitting (time varying parameters)

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
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Optimizing SIR Model Parameters in R with Time Varying Beta

In the realm of infectious disease modeling, the SIR (Susceptible, Infected, Recovered) model is a foundational framework used to understand how diseases spread through populations. An intriguing challenge arises when attempting to incorporate time-varying transmission rates, often represented by the parameter beta. This guide delves into a common issue faced by R users: how to optimize SIR model parameters with dynamic beta values effectively.

Understanding the Problem

As part of an analytical exercise, you're trying to fit an SIR model in R that allows for a time-varying transmission rate. The objective is to estimate the beta parameter at each time step using observed data.

You've made commendable progress by implementing a framework that simulates the SIR model and incorporates real data. However, you're facing a specific issue: despite changing initial values for beta, the fitting function (modFit) continues to return the same value (0.8) regardless of your inputs. This leads to confusion regarding whether your approach to parameter estimation is appropriate.

Breaking Down the Solution

1. Reassess the Fitting Approach

The primary concern with attempting to estimate beta at every single time step is that a dynamic model may not handle excessive fluctuations well. Here are some suggestions to improve the fitting process:

Limit the Number of Knots: Instead of using a time-dependent function with many knots (such as 20 different values for each time step), use a simpler representation like:

Linear

Quadratic

Spline with fewer knots (between 3-5)

This helps stabilize the estimation process, simplifying the optimization task.

2. Modify the Implementation

Make use of libraries like deSolve and FME while adapting your model for efficiency. Here’s an improved approach to optimizing beta with constraints:

[[See Video to Reveal this Text or Code Snippet]]

3. Analyze and Interpret Results

After applying these adjustments, analyze the fitting results:

Performance Comparison: Generate visualizations that compare your model output with real data to understand how closely your estimates track observed trends.

Stability and Trends: Investigate how variations in beta influence model predictions. It's essential to explore robustness in your final estimates.

Conclusion

Incorporating time-varying parameters into models like the SIR can be complex but rewarding. By simplifying your fitting approach and employing appropriate constraints, you can achieve more effective parameter estimation and ultimately improve your model's predictive capability.

Always remember that model fitting is both a science and an art; it requires experimentation and adaptability to discover what works best for your specific scenario. Happy coding in R!

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