Detecting Robust Patterns in the Spread of Epidemics: A Case Study of Influenza in the United States and France

TL;DR

This paper introduces a method to detect correlations in epidemic spread due to human movement, applied to influenza data in the US and France, revealing travel-related transmission patterns and the potential impact of travel restrictions.

Contribution

The paper presents a novel method for identifying travel-induced epidemic correlations and applies it to real influenza data, uncovering distinct regional spread patterns.

Findings

Strong short-range correlations in US states

Long-range spread linked to air traffic in US

No dominant transportation mode in France

Abstract

In this paper, the authors develop a method of detecting correlations between epidemic patterns in different regions that are due to human movement and introduce a null model in which the travel-induced correlations are cancelled. They apply this method to the well-documented cases of seasonal influenza outbreaks in the United States and France. In the United States (using data for 1972-2002), the authors observed strong short-range correlations between several states and their immediate neighbors, as well as robust long-range spreading patterns resulting from large domestic air-traffic flows. The stability of these results over time allowed the authors to draw conclusions about the possible impact of travel restrictions on epidemic spread. The authors also applied this method to the case of France (1984-2004) and found that on the regional scale, there was no transportation mode that clearly dominated disease spread. The simplicity and robustness of this method suggest that it could be a useful tool for detecting transmission channels in the spread of epidemics.