Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study

<p>Abstract</p> <p>Background</p> <p>The global spread of the severe acute respiratory syndrome (SARS) epidemic has clearly shown the importance of considering the long-range transportation networks in the understanding of emerging diseases outbreaks. The introduction o...

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Main Authors: Barthélemy Marc, Barrat Alain, Colizza Vittoria, Vespignani Alessandro
Format: Article
Language:English
Published: BMC 2007-11-01
Series:BMC Medicine
Online Access:http://www.biomedcentral.com/1741-7015/5/34
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spelling doaj-1498f7cf6fe148c98611ec070d5e33fe2020-11-24T22:50:04ZengBMCBMC Medicine1741-70152007-11-01513410.1186/1741-7015-5-34Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case studyBarthélemy MarcBarrat AlainColizza VittoriaVespignani Alessandro<p>Abstract</p> <p>Background</p> <p>The global spread of the severe acute respiratory syndrome (SARS) epidemic has clearly shown the importance of considering the long-range transportation networks in the understanding of emerging diseases outbreaks. The introduction of extensive transportation data sets is therefore an important step in order to develop epidemic models endowed with realism.</p> <p>Methods</p> <p>We develop a general stochastic meta-population model that incorporates actual travel and census data among 3 100 urban areas in 220 countries. The model allows probabilistic predictions on the likelihood of country outbreaks and their magnitude. The level of predictability offered by the model can be quantitatively analyzed and related to the appearance of robust epidemic pathways that represent the most probable routes for the spread of the disease.</p> <p>Results</p> <p>In order to assess the predictive power of the model, the case study of the global spread of SARS is considered. The disease parameter values and initial conditions used in the model are evaluated from empirical data for Hong Kong. The outbreak likelihood for specific countries is evaluated along with the emerging epidemic pathways. Simulation results are in agreement with the empirical data of the SARS worldwide epidemic.</p> <p>Conclusion</p> <p>The presented computational approach shows that the integration of long-range mobility and demographic data provides epidemic models with a predictive power that can be consistently tested and theoretically motivated. This computational strategy can be therefore considered as a general tool in the analysis and forecast of the global spreading of emerging diseases and in the definition of containment policies aimed at reducing the effects of potentially catastrophic outbreaks.</p> http://www.biomedcentral.com/1741-7015/5/34
collection DOAJ
language English
format Article
sources DOAJ
author Barthélemy Marc
Barrat Alain
Colizza Vittoria
Vespignani Alessandro
spellingShingle Barthélemy Marc
Barrat Alain
Colizza Vittoria
Vespignani Alessandro
Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study
BMC Medicine
author_facet Barthélemy Marc
Barrat Alain
Colizza Vittoria
Vespignani Alessandro
author_sort Barthélemy Marc
title Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study
title_short Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study
title_full Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study
title_fullStr Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study
title_full_unstemmed Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study
title_sort predictability and epidemic pathways in global outbreaks of infectious diseases: the sars case study
publisher BMC
series BMC Medicine
issn 1741-7015
publishDate 2007-11-01
description <p>Abstract</p> <p>Background</p> <p>The global spread of the severe acute respiratory syndrome (SARS) epidemic has clearly shown the importance of considering the long-range transportation networks in the understanding of emerging diseases outbreaks. The introduction of extensive transportation data sets is therefore an important step in order to develop epidemic models endowed with realism.</p> <p>Methods</p> <p>We develop a general stochastic meta-population model that incorporates actual travel and census data among 3 100 urban areas in 220 countries. The model allows probabilistic predictions on the likelihood of country outbreaks and their magnitude. The level of predictability offered by the model can be quantitatively analyzed and related to the appearance of robust epidemic pathways that represent the most probable routes for the spread of the disease.</p> <p>Results</p> <p>In order to assess the predictive power of the model, the case study of the global spread of SARS is considered. The disease parameter values and initial conditions used in the model are evaluated from empirical data for Hong Kong. The outbreak likelihood for specific countries is evaluated along with the emerging epidemic pathways. Simulation results are in agreement with the empirical data of the SARS worldwide epidemic.</p> <p>Conclusion</p> <p>The presented computational approach shows that the integration of long-range mobility and demographic data provides epidemic models with a predictive power that can be consistently tested and theoretically motivated. This computational strategy can be therefore considered as a general tool in the analysis and forecast of the global spreading of emerging diseases and in the definition of containment policies aimed at reducing the effects of potentially catastrophic outbreaks.</p>
url http://www.biomedcentral.com/1741-7015/5/34
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