A model-based approach to assess epidemic risk

TL;DR

This paper presents a comprehensive model combining mobility networks and epidemic dynamics to assess global epidemic risk and optimize mitigation strategies like airport closures.

Contribution

It introduces a novel framework integrating a SEIRS epidemic model with a graph diffusion approach and an optimization method for epidemic mitigation.

Findings

The model accurately simulates COVID-19 spread scenarios.

Optimization identifies effective airport closure strategies.

Framework aids timely decision-making for epidemic control.

Abstract

We study how international flights can facilitate the spread of an epidemic to a worldwide scale. We combine an infrastructure network of flight connections with a population density dataset to derive the mobility network, and then we define an epidemic framework to model the spread of the disease. Our approach combines a compartmental SEIRS model with a graph diffusion model to capture the clusteredness of the distribution of the population. The resulting model is characterised by the dynamics of a metapopulation SEIRS, with amplification or reduction of the infection rate which is determined also by the mobility of individuals. We use simulations to characterise and study a variety of realistic scenarios that resemble the recent spread of COVID-19. Crucially, we define a formal framework that can be used to design epidemic mitigation strategies: we propose an optimisation approach based on genetic algorithms that can be used to identify an optimal airport closure strategy, and that can be employed to aid decision making for the mitigation of the epidemic, in a timely manner.