Several approaches have been proposed for near real-time detection and prediction of the spread of influenza. These include search query data for influenza-related terms, which has been explored as a tool for augmenting traditional surveillance methods. In this paper, we present a method that uses Internet search query data from Baidu to model and monitor influenza activity in China. The objectives of the study are to present a comprehensive technique for: (i) keyword selection, (ii) keyword filtering, (iii) index composition and (iv) modeling and detection of influenza activity in China. Sequential time-series for the selected composite keyword index is significantly correlated with Chinese influenza case data. In addition, one-month ahead prediction of influenza cases for the first eight months of 2012 has a mean absolute percent error less than 11%. To our knowledge, this is the first study on the use of search query data from Baidu in conjunction with this approach for estimation of influenza activity in China.

Mass excesses of short-lived A=2Z-1 nuclei (63)Ge, (65)As, (67)Se, and (71)Kr have been directly measured to be -46,921(37), -46,937(85), -46,580(67), and -46,320(141)  keV, respectively. The deduced proton separation energy of -90(85)  keV for (65)As shows that this nucleus is only slightly proton unbound. X-ray burst model calculations with the new mass excess of (65)As suggest that the majority of the reaction flow passes through (64)Ge via proton capture, indicating that (64)Ge is not a significant rp-process waiting point.

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