Influenza epidemics arise through the accumulation of viral genetic changes. The emergence of new virus strains coincides with a higher level of influenza-like illness (ILI), which is seen as a peak of a normal season. Monitoring the spread of an epidemic influenza in populations is a difficult and important task. Twitter is a free social networking service whose messages can improve the accuracy of forecasting models by providing early warnings of influenza outbreaks. In this study, we have examined the use of information embedded in the Hangeul Twitter stream to detect rapidly evolving public awareness or concern with respect to influenza transmission and developed regression models that can track levels of actual disease activity and predict influenza epidemics in the real world. Our prediction model using a delay mode provides not only a real-time assessment of the current influenza epidemic activity but also a significant improvement in prediction performance at the initial phase of ILI peak when prediction is of most importance.

Influenza virus infects host cells through membrane fusion, a process mediated by the low pH-induced conformational change of the viral surface glycoprotein haemagglutinin (HA). We determined the structures and biochemical properties of the HA proteins from A/Korea/01/2009 (KR01), a 2009 pandemic strain, and A/Thailand/CU44/2006 (CU44), a seasonal strain. The crystal structure of KR01 HA revealed a V-shaped head-to-head arrangement, which is not seen in other HA proteins including CU44 HA. We isolated a broadly neutralizing H1-specific monoclonal antibody GC0757. The KR01 HA-Fab0757 complex structure also exhibited a headto-head arrangement of HA. Both native and Fab complex structures reveal a different spatial orientation of HA1 relative to HA2, indicating that HA is flexible and dynamic at neutral pH. Further, the KR01 HA exhibited significantly lower protein stability and increased susceptibility to proteolytic cleavage compared with other HAs. Our structures provide important insights into the conformational flexibility of HA.

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