Global disease spread: statistics and estimation of arrival times

TL;DR

This paper develops a model to estimate disease arrival times across interconnected subpopulations, like cities, using network properties, and demonstrates its accuracy on the global airport network for predicting epidemic spread.

Contribution

It introduces a new, easily computable metric to approximate disease arrival times in large-scale metapopulation networks, validated on real-world data.

Findings

The proposed metric accurately predicts the order of disease arrival in different subpopulations.

The method works well on the worldwide airport network example.

The metric can help identify dominant pathways of disease spread.

Abstract

We study metapopulation models for the spread of epidemics in which different subpopulations (cities) are connected by fluxes of individuals (travelers). This framework allows to describe the spread of a disease on a large scale and we focus here on the computation of the arrival time of a disease as a function of the properties of the seed of the epidemics and of the characteristics of the network connecting the various subpopulations. Using analytical and numerical arguments, we introduce an easily computable quantity which approximates this average arrival time. We show on the example of a disease spread on the world-wide airport network that this quantity predicts with a good accuracy the order of arrival of the disease in the various subpopulations in each realization of epidemic scenario, and not only for an average over realizations. Finally, this quantity might be useful in the identification of the dominant paths of the disease spread.