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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting.

João Pedro Valeriano¹, Pedro Henrique Cintra², Gustavo Libotte^3,4

¹Instituto de Física Teórica, Universidade Estadual Paulista, R. Dr. Bento Teobaldo Ferraz, 271, Bloco 2, Barra Funda, São Paulo, SP 01140-070 Brazil.

Nonlinear Dynamics

|October 3, 2022

Summary

This summary is machine-generated.

This study introduces a novel Bayesian learning framework to analyze complex COVID-19 epidemic waves. The method improves forecasting accuracy by sequentially updating parameters using approximate Bayesian computation (ABC).

Keywords:

Approximate Bayesian computation Covid-19 Epidemic forecasting SEIRD model

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Area of Science:

Epidemiology
Mathematical Modeling
Computational Statistics

Background:

The COVID-19 pandemic exhibited complex epidemic waves, challenging traditional modeling approaches.
Simple compartmental models are insufficient for analyzing intricate infection dynamics and generating reliable forecasts.
Advanced mathematical techniques are crucial for understanding and predicting epidemic trajectories.

Purpose of the Study:

To propose a novel framework for analyzing complex dynamical systems, specifically epidemic data.
To enhance the performance of approximate Bayesian computation (ABC) for parameter fitting in time-series analysis.
To improve short-term forecasting of infectious disease outbreaks.

Main Methods:

A time-windowing approach to divide and analyze epidemic data sequentially.
Application of approximate Bayesian computation (ABC) for parameter estimation within each time window.
A Bayesian learning strategy where posterior distributions from one window inform the prior distributions of the next.

Main Results:

The proposed framework effectively analyzes complex epidemic dynamics, such as COVID-19 waves.
The Bayesian learning approach significantly improves the performance of ABC algorithms.
The method demonstrates capability for accurate short-term forecasting of COVID-19 cases across multiple countries.

Conclusions:

The sequential Bayesian learning framework offers a robust method for analyzing complex epidemiological data.
This approach enhances the reliability of parameter estimation and forecasting in dynamic systems.
The framework provides a valuable tool for public health surveillance and response during pandemics.