Forecast and control of epidemics in a globalized world.

By L. Hufnagel, Dirk Brockmann, Theo Geisel. Proceedings of the National Academy of Sciences of the United States of America, 2004

1,077 citations recorded by the source corpus.

The rapid worldwide spread of severe acute respiratory syndrome demonstrated the potential threat an infectious disease poses in a closely interconnected and interdependent world. Here we introduce a probabilistic model that describes the worldwide spread of infectious diseases and demonstrate that a forecast of the geographical spread of epidemics is indeed possible. This model combines a stochastic local infection dynamics among individuals with stochastic transport in a worldwide network, taking into account national and international civil aviation traffic. Our simulations of the severe acute respiratory syndrome outbreak are in surprisingly 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 spread 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 inhibiting the global spread of epidemics.

Subfields: Genetics, Modeling and Simulation, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/54bafaf775a175e3665e78939ded44bf9bfb4931

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Modeling and Simulation

In Public Health, Environmental and Occupational Health

Citations in the corpus

Cross-Species Virus Transmission and the Emergence of New Epidemic Diseases (2008, background): “Such “superspreading” individuals were documented during the SARS CoV epidemic and during outbreaks of measles and other aerosolized viruses (75, 89, 135).”
Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models (2010, methodology): “The Global Epidemic and Mobility (GLEaM) model is based on a metapopulation approach [15-21] in which the world is divided into geographical regions defining a subpopulation network where connections among subpopulations represent the individual fluxes due to the transportation and mobility infrastr”
Saving Human Lives: What Complexity Science and Information Systems can Contribute (2014, neutral): “(19) and most sophisticated models, such as the GLEAM simulation framework possess an underlying mathematical machinery of similar nature [115, 120, 132, 133].”
Image sequence geolocation with human travel priors (2009, background): “Airline traffic data provides another source of aggregate travel data [6, 13], but for only one mode of transport.”
Digital Epidemiology (2012, background): “This was also manifested by slower geographic dissemination of diseases [14,15].”
Urbanisation and infectious diseases in a globalised world (2011, background): “135 Conventional health strategies should be adapted according to the dynamics of urbanisation and to the distinctive epidemiological pattern of transmittable diseases in cities.”
Natural human mobility patterns and spatial spread of infectious diseases (2011, background): “These types of models have been employed in numerous recent studies [2,14,24,25].”
Understanding the Spreading Patterns of Mobile Phone Viruses (2009, background): “First, a Bluetooth virus can infect all Bluetooth-activated phones within a distance from 10 to 30m, resulting in a spatially localized spreading pattern similar to the one observed in the case of influenza (3, 6, 7), SARS (8, 9) and other contact-based diseases (10) (Fig.”
Multiscale mobility networks and the spatial spreading of infectious diseases (2009, background): “INTRODUCTION Computational approaches to the realistic modeling of spatial epidemic spread make use of a wide array of simulation schemes [1] ranging from very detailed agent based approaches [2, 3, 4, 5, 6] to structured metapopulation models based on data-driven mobility schemes at the inter-popul”
The Emergence of Global Systemic Risk (2015, neutral): “F or p er so na l u se o nl y. Hufnagel et al. 2004, Kilpatrick & Randolph 2012, McMichael et al. 2006, Tatem et al. 2006).”
A Tale of Many Cities: Universal Patterns in Human Urban Mobility (2011, neutral): “Webster, Textbook of Influenza (Blackwell, Malden, Massachusetts, 1998) [11] L.”
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees (2011, background): “Knowledge of contact patterns is crucial for building and informing computational models of infectious disease transmission [14-23].”