Incorporating Dynamic Flight Network in SEIR to Model Mobility between Populations

TL;DR

This paper introduces Flight-SEIR, a modified epidemiological model that incorporates dynamic flight network data to better predict COVID-19 spread through travel, enabling improved outbreak detection and policy evaluation.

Contribution

It presents a novel extension of the SEIR model that accounts for air traffic and source positivity rates, enhancing predictive accuracy and policy assessment.

Findings

Enhanced early outbreak detection capabilities

More accurate estimation of reproduction number

Effective evaluation of travel restriction impacts

Abstract

Current efforts of modelling COVID-19 are often based on the standard compartmental models such as SEIR and their variations. As pre-symptomatic and asymptomatic cases can spread the disease between populations through travel, it is important to incorporate mobility between populations into the epidemiological modelling. In this work, we propose to modify the commonly-used SEIR model to account for the dynamic flight network, by estimating the imported cases based on the air traffic volume as well as the test positive rate at the source. This modification, called Flight-SEIR, can potentially enable 1). early detection of outbreaks due to imported pre-symptomatic and asymptomatic cases, 2). more accurate estimation of the reproduction number and 3). evaluation of the impact of travel restrictions and the implications of lifting these measures. The proposed Flight-SEIR is essential in navigating through this pandemic and the next ones, given how interconnected our world has become.