disease. Each individual infected with influenza virus, for example, has the potential to spread the virus to its neighborhood. Observe also that the random nature of lðtÞ in Eq. (3) is not essential to obtain the results described below. However, the random nature of lðtÞ mimics possible fluctuations which are common in real systems. A typical simulation of this model is shown in Fig. 4 for a particular choice of parameters. Observe that the model predicts qualitatively several aspects of the empirical results – the exponential growth after an initial transient, the linear growth in the number of countries with confirmed cases and a cumulative distribution exhibiting a systematic curvature on log-log scale indicating lognormal behavior. In this study, we identify spreading patterns of the 2009 influenza A (H1N1/S-OIV) pandemic for a particular time period. 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As Swine Flu Circles Globe, Scientists Grapple With Basic Questions. Science 324: 572–3. 10. World Health Organization (WHO). Situation updates - Pandemic (H1N1) (2009) http://www.who.int/csr/disease/swineflu/updates. Accessed 2010 March 15. 11. Gurevich KG (2009) Formal kinetics of H1N1 epidemic. Theor Biol Med Model 6: 23–5. Spreading Patterns of the Influenza A The future evolution of any influenza pandemic is difficult to predict. However, the analysis of epidemiological data and the selection of realistic models may provide some insight in the spatial and temporal evolution of pandemic events. Models for the spread of infectious diseases are useful tools - can project plausible scenarios, guide control strategies, suggests roles of antiviral drugs and vaccines and so on. Our empirical results could help to select realistic models of the dynamics of influenza-type pandemic events. Supporting Information Figure S1 Cumulative distribution of cases among countries for all data. Empirical cumulative distribution, R(y), of laboratory-confirmed cases y for all data (48 days within the period of 69 days, from 26 April to 3 July, 2009). The data are shown from smaller to larger times – from left to right and from top to bottom. Observe the gradual convergence from a linear behavior on log-log scale (power law behavior) to curves with a modest negative curvature everywhere (log-normal behavior). (TIF) Author Contributions Conceived and designed the experiments: RdSM LCM RPBdS. Performed the experiments: JJVT HVR. Analyzed the data: SdPJ HVR. Contributed reagents/materials/analysis tools: JJVT. Wrote the paper: SdPJ LCM RdSM. 12. Sornette D (2006) Critical phenomena in natural sciences: chaos, fractals, selforganization and disorder: concepts and tools. Berlin Heidelberg: Springer- Verlag press. 525 p. 13. Rhodes CJ, Anderson RM (1996) Power laws governing epidemics in isolated populations. Nature 381: 600–2. 14. 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Liao C-M, Chang S-Y, Chen S-C, Chio C-P (2009) Influenza-associated morbidity in subtropical Taiwan. Int J Infect Dis13: 589–99. PLoS ONE | www.plosone.org 4 March 2011 | Volume 6 | Issue 3 | e17823