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

TL;DR

This paper introduces a sequential Bayesian learning framework using approximate Bayesian computation for epidemic forecasting, effectively capturing complex COVID-19 dynamics through time-window analysis and improving short-term predictions.

Contribution

It presents a novel sequential ABC-based approach that updates parameter estimates across time-windows, enhancing epidemic modeling accuracy.

Findings

Improved ABC performance with sequential data incorporation

Effective short-term COVID-19 case forecasting

Applicable to multiple countries' epidemic data

Abstract

The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an Approximate Bayesian Computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting.