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A Data-Driven Approach to Quantifying Immune States in Sepsis

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Published on: February 7, 2025

Data driven computing by the morphing fast Fourier transform ensemble Kalman filter in epidemic spread simulations.

Jan Mandel¹, Jonathan D Beezley, Loren Cobb

¹Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80217-3364, USA.

Procedia Computer Science

|October 30, 2010

Summary

A new Fast Fourier Transform Ensemble Kalman Filter (FFT EnKF) method improves epidemic simulations. This computationally inexpensive approach accurately tracks epidemic spread using a small ensemble and morphing techniques.

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Area of Science:

Epidemiology
Computational Biology
Data Assimilation

Background:

Stochastic cell simulations are crucial for understanding epidemic dynamics.
Traditional data assimilation methods can be computationally intensive for real-time epidemic tracking.
Accurate modeling of epidemic spread requires incorporating spatial information and dynamic changes.

Purpose of the Study:

To introduce and evaluate the Fast Fourier Transform Ensemble Kalman Filter (FFT EnKF) for epidemic simulations.
To develop a computationally efficient data assimilation method for stochastic epidemic models.
To enhance the tracking of epidemic spatial changes using ensemble filtering.

Main Methods:

The study proposes the FFT EnKF, integrating spatial statistics with ensemble filtering.
It applies the FFT EnKF to a stochastic cell-based simulation of the S-I-R (Susceptible-Infectious-Recovered) epidemic model.
The method is combined with morphing Ensemble Kalman Filter (EnKF) to assimilate positional shifts of the epidemic.

Main Results:

The FFT EnKF provides a localized and computationally inexpensive alternative to standard EnKF.
The method effectively utilizes a very small ensemble size for data assimilation.
Assimilation of epidemic position changes was successfully achieved by combining FFT EnKF with morphing EnKF.

Conclusions:

The FFT EnKF is a promising data assimilation technique for stochastic epidemic simulations.
Its computational efficiency and accuracy make it suitable for real-time epidemic monitoring.
The integration with morphing EnKF allows for dynamic tracking of epidemic spatial progression.