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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. [ABSTRACT FROM AUTHOR]

Background: Epidemics of HFMD are elevated every year globally, especially in mainland China. The disease now presents as an increasing threat to public health worldwide. Methods: Five hundred and seventy-one EV71-infected HFMD patients in Beijing You'an Hospital were grouped by disease severity: Mild (no severe complication) (n = 221), and Severe group (complicated with brainstem encephalitis (BE), and/or pulmonary edema (PE) (n = 350)). Clinical and laboratory findings and levels of 7 serum cytokines were analyzed. Results: Univariate analysis showed that (RR)>26/min (p<0.001), age<4 yo (p = 0.001), GLU>8.3 mmol/L (p = 0.008), CL<98 mmol/L (p = 0.026), and WBC>1.2×109/L (p = 0.040) were associated with severe cases. Results of multivariate analysis indicated five independent risk factors (RR>26/min (p<0.001), Age<4 yo (p<0.001), GLU>8.3 mmol/L (p = 0.011), LYM>40% (p = 0.010), and ALT>40 U/L (p = 0.045)). In addition to single-factor analysis, we further analyzed the use of different combinations of risk factors. “GLU>8.3 and CL<98 and RR>26” (confidence ration (CR) = 100%) is the top indicator, followed by “ALT>40 and LYM>40% and RR>26 and Age<4 yo” (CR = 92.9%). Serum levels of IL-2, IL-4, IL-10, IFNγ, GM-CSF, and TNFα were higher in severe cases than in mild cases. A new evaluation scoring system by scoring each risk factor 1 and independent risk factor 2 was developed for early identification of severe HFMD cases. Conclusions: Five independent risk factors, along with indicative combinations of risk factors, for severe cases were identified, and a scoring system was created to facilitate the use of indicators for early medical intervention. [ABSTRACT FROM AUTHOR]