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By Daniel Zeng, Ivan Gotham, Ken Komatsu, Cecil Lynch, Mark Thurmond, David Madigan, Bill Lober, James Kvach, Hsinchun Chen

ISBN-10: 3540726071

ISBN-13: 9783540726074

ISBN-10: 354072608X

ISBN-13: 9783540726081

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Read Online or Download Intelligence and Security Informatics: Biosurveillance: Second NSF Workshop, BioSurveillance 2007, New Brunswick, NJ, USA, May 22, 2007, Proceedings (Lecture ... Applications, incl. Internet Web, and HCI) PDF

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Extra resources for Intelligence and Security Informatics: Biosurveillance: Second NSF Workshop, BioSurveillance 2007, New Brunswick, NJ, USA, May 22, 2007, Proceedings (Lecture ... Applications, incl. Internet Web, and HCI)

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Sensitivity is also reasonably good, which means that the system can catch most positive cases in the reference standard. Specificity of most syndromes is close to one because 1) most syndromes have low prevalence and 2) the system in general can successfully mark negative cases. Overall, the Chinese CC classification system shows promising performance. Table 3. 9906 Comparing the performance among syndromic categories, fever symptom has the best performance with PPV, sensitivity, and specificity of nearly one.

Daniel Zeng is an affiliated professor at the Institute of Automation, the Chinese Academy of Sciences, and wishes to acknowledge support from a research grant (60573078) from the National Natural Science Foundation of China, an international collaboration grant (2F05N01) from the Chinese Academy of Sciences, and a National Basic Research Program of China (973) grant (2006CB705500) from the Ministry of Science and Technology. References 1. : Generating a Reliable Reference Standard Set for Syndromic Case Classification.

The general form of the Gaussian model is as follows: 2 Yi (t) = ai0 e−bi0 t + aij e j=1 − t−bij cij 2 . (1) A Model for Characterizing Annual Flu Cases 5 10 x 10 39 France: 1985−2005 Influenza Like Illness (ILI) 9 8 7 6 5 4 3 2 1 0 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 Time (years) Fig. 1. Influenza–Like–Illness (ILI) weekly data for France (1985–2005) Parameter ai0 estimates the baseline (at time zero) of i–th year outbreak, ai1 and ai2 estimate the intensity of each peak in the i–th year, bi0 estimate the decay rate for the background model, bi1 and bi2 evaluate the time of each peak, and ci1 and ci2 describe the duration of these outbreaks.

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Intelligence and Security Informatics: Biosurveillance: Second NSF Workshop, BioSurveillance 2007, New Brunswick, NJ, USA, May 22, 2007, Proceedings (Lecture ... Applications, incl. Internet Web, and HCI) by Daniel Zeng, Ivan Gotham, Ken Komatsu, Cecil Lynch, Mark Thurmond, David Madigan, Bill Lober, James Kvach, Hsinchun Chen


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