Inferring unobserved multistrain epidemic sub-populations using synchronization dynamics

TL;DR

This paper introduces a novel synchronization-based method to infer unobserved primary infection levels in multistrain epidemics like dengue by analyzing the dynamics between observed secondary infections and unobserved primary infections.

Contribution

The paper develops a new approach using synchronization dynamics and center manifold theory to estimate unobserved epidemic sub-populations from observed data.

Findings

Synchronization accurately predicts primary infectives from secondary data.

Center manifold equations relate driven and driver epidemic systems.

Numerical stability is confirmed via Lyapunov exponents.

Abstract

A new method is proposed to infer unobserved epidemic sub-populations by exploiting the synchronization properties of multistrain epidemic models. A model for dengue fever is driven by simulated data from secondary infective populations. Primary infective populations in the driven system synchronize to the correct values from the driver system. Most hospital cases of dengue are secondary infections, so this method provides a way to deduce unobserved primary infection levels. We derive center manifold equations that relate the driven system to the driver system and thus motivate the use of synchronization to predict unobserved primary infectives. Synchronization stability between primary and secondary infections is demonstrated through numerical measurements of conditional Lyapunov exponents and through time series simulations.