Forecast and Control of Epidemics in a Globalized World

TL;DR

This paper introduces a probabilistic model combining local infection dynamics and global transportation networks to forecast and control the worldwide spread of epidemics like SARS, emphasizing the importance of rapid, targeted responses.

Contribution

The paper presents a novel stochastic model integrating local infection and international travel data to predict epidemic spread and evaluate control strategies.

Findings

Model accurately simulates SARS spread

High predictability due to network heterogeneity

Rapid, targeted interventions are most effective

Abstract

The rapid worldwide spread of the severe acute respiratory syndrome (SARS) demonstrated the potential threat an infectious disease poses in a closely interconnected and interdependent world. Here we introduce a probabilistic model which describes the worldwide spreading of infectious diseases and demonstrate that a forecast of the geographical spread of epidemics is indeed possible. It combines a stochastic local infection dynamics between individuals with stochastic transport in a worldwide network which takes into account the national and international civil aviation traffic. Our simulations of the SARS outbreak are in suprisingly good agreement with published case reports. We show that the high degree of predictability is caused by the strong heterogeneity of the network. Our model can be used to predict the worldwide spreading of future infectious diseases and to identify endangered regions in advance. The performance of different control strategies is analyzed and our simulations show that a quick and focused reaction is essential to inhibit the global spreading of epidemics.