SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics

TL;DR

SPADE4 is a novel forecasting method for epidemics that leverages delay embedding and sparse regression to predict disease trajectories from limited data, outperforming traditional compartmental models.

Contribution

It introduces a data-driven approach combining delay embedding and sparsity to improve epidemic forecasting without detailed system knowledge.

Findings

Outperforms traditional models on simulated data

Effective with scarce and incomplete data

Captures underlying system dynamics from observations

Abstract

Predicting the evolution of diseases is challenging, especially when the data availability is scarce and incomplete. The most popular tools for modelling and predicting infectious disease epidemics are compartmental models. They stratify the population into compartments according to health status and model the dynamics of these compartments using dynamical systems. However, these predefined systems may not capture the true dynamics of the epidemic due to the complexity of the disease transmission and human interactions. In order to overcome this drawback, we propose Sparsity and Delay Embedding based Forecasting (SPADE4) for predicting epidemics. SPADE4 predicts the future trajectory of an observable variable without the knowledge of the other variables or the underlying system. We use random features model with sparse regression to handle the data scarcity issue and employ Takens' delay embedding theorem to capture the nature of the underlying system from the observed variable. We show that our approach outperforms compartmental models when applied to both simulated and real data.