Percolación de la epidemia de influenza AH1N1 en el mundo: Utilidad de los modelos predictivos basados en conectividad espacial
Background: The 2009 AH1N1 epidemics expanded rapidly around the world by the current connectivity conditions. The spread of epidemics can be described by the phenomenon of percolation, that allows the estimation of the threshold conditions that produce connectivity between different regions and tha...
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| Autores principales: | , |
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| Lenguaje: | Spanish / Castilian |
| Publicado: |
Sociedad Médica de Santiago
2010
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0034-98872010000500007 |
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| Sumario: | Background: The 2009 AH1N1 epidemics expanded rapidly around the world by the current connectivity conditions. The spread of epidemics can be described by the phenomenon of percolation, that allows the estimation of the threshold conditions that produce connectivity between different regions and that has been used to describe physical and ecological phenomena. Aim: To analyze the spread of AH1N1 epidemic based on information from the WHO. Material and Methods: The world was considered as composed of a set of countries and regular cells. The moment when the percolation occurred was analyzed and logistic regressions were adjusted to the change in the proportion of infected units versus time, comparing predicted and observed rates. Results: Percolation occurred in America on day 15, in Eurasia on day 32 and in the world on day 74. The models showed adequate predictive capacity. The predictions for the percolation of the epidemic in the world varied between days 66 and 75. The prediction based on countries was better than that based on cells. Conclusions: These results show that percolation theory fts well to the spread of epidemics. Predictions based only on data on-off (infected non infected) and in the progression of the proportion of infected cells are a good way of predicting the spread of an epidemic and when this crosses a region geographically. |
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