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

摘要

Background: The global spread of the severe acute respiratory syndrome (SARS)epidemic has clearly shown the importance of considering the long-rangetransportation networks in the understanding of emerging diseases outbreaks.The introduction of extensive transportation data sets is therefore animportant step in order to develop epidemic models endowed with realism.Methods: We develop a general stochastic meta-population model thatincorporates actual travel and census data among 3 100 urban areas in 220countries. The model allows probabilistic predictions on the likelihood ofcountry outbreaks and their magnitude. The level of predictability offered bythe model can be quantitatively analyzed and related to the appearance ofrobust epidemic pathways that represent the most probable routes for the spreadof the disease. Results: In order to assess the predictive power of the model,the case study of the global spread of SARS is considered. The diseaseparameter values and initial conditions used in the model are evaluated fromempirical data for Hong Kong. The outbreak likelihood for specific countries isevaluated along with the emerging epidemic pathways. Simulation results are inagreement with the empirical data of the SARS worldwide epidemic. Conclusions:The presented computational approach shows that the integration of long-rangemobility and demographic data provides epidemic models with a predictive powerthat can be consistently tested and theoretically motivated. This computationalstrategy can be therefore considered as a general tool in the analysis andforecast of the global spreading of emerging diseases and in the definition ofcontainment policies aimed at reducing the effects of potentially catastrophicoutbreaks.