Impact of temporal scales and recurrent mobility patterns on the unfolding of epidemics

TL;DR

This paper develops a Markovian model to analyze how recurrent human mobility patterns at different temporal scales influence the emergence of epidemics, providing analytical insights into epidemic thresholds.

Contribution

It introduces a novel Markovian framework that incorporates recurrent mobility patterns and temporal scales to predict epidemic outbreaks.

Findings

The model accurately predicts epidemic thresholds aligning with simulations.

Temporal scales of mobility and epidemiology critically affect disease emergence.

Analytical expressions for epidemic thresholds reveal key conditions for outbreaks.

Abstract

Human mobility plays a key role on the transformation of local disease outbreaks into global pandemics. Thus, the inclusion of human movements into epidemic models has become mandatory for understanding current epidemic episodes and to design efficient prevention policies. Following this challenge, here we develop a Markovian framework which enables to address the impact of recurrent mobility patterns on the epidemic onset at different temporal scales. This formalism is validated by comparing their predictions with results from mechanistic simulations. The fair agreement between both theory and simulations enables to get an analytical expression for the epidemic threshold which captures the critical conditions triggering epidemic outbreaks. Finally, by performing an exhaustive analysis of this epidemic threshold, we reveal that the impact of tuning human mobility on the emergence of diseases is strongly affected by the temporal scales associated to both epidemiological and mobility processes.