By Daniel Zeng, Ivan Gotham, Ken Komatsu, Cecil Lynch, Mark Thurmond, David Madigan, Bill Lober, James Kvach, Hsinchun Chen
Read 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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Additional 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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Table 2 also shows that H3N2 outbreaks tend to peak sooner than H1N1 and B. We carried out a similar test to assess any significant difference among the peak times for H3N2 and those for H1N1 and B and find that peak times for H3N2 subtype outbreaks occur earlier than H1N1 and B. Note that these results are supported by the parameter estimates obtained from fitting Model 1 and Model 2. For each subtype dominant season, our goodness of fit results (adjusted R2 ) showed that the latter model improves the fit.
Chen et al. Table 3. Types of Contacts in the Investigation Personal Contacts Geographical Contacts − − − − − − − − − Family Member Roommate Colleague/Classmate Close Contact Foreign Country Travel Hospital Visit High-Risk Area Visit Hospital Admission History Workplace Since a constructed contact network is a multi-mode network which has three kinds of nodes, patients, areas, and hospitals, we need to transform the constructed network into a one-mode network which has only one kind of nodes, patients.
One of the aims of flu surveillance is the early detection of outbreaks, however, understanding the underlying mechanisms driving the observed fluctuations can be instrumental in developing effective monitoring systems. We study a time series regression model that summarizes outbreak trends and describes the observed seasonality. We apply the model to influenza–like–illness (ILI) weekly data for France reported during 1985–2005. We estimate the model parameters through least squares and validated the model numerically (adjusted R2 ) and graphically (residual analysis).