Nick J. Pizzi

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BACKGROUND Initial public health responses to the 2009 influenza H1N1 pandemic were based on difficult decisions in the face of substantial uncertainty. Policy effectiveness depends critically on such decisions, and future planning for maximum protection of community health requires understanding of the impact of public health responses in observed(More)
From the Black Death of 1347–1350 (Murray, 2007) and the Spanish influenza pandemic of 1918–1919 (Taubenberger & Morens, 2006), to the more recent 2003 SARS outbreak (Lingappa et al., 2004) and the 2009 influenza pandemic (Moghadas et al., 2009), as well as countless outbreaks of childhood infections, infectious diseases have been the bane of humanity(More)
BACKGROUND Given the enormity of challenges involved in pandemic preparedness, design and implementation of effective and cost-effective public health policies is a major task that requires an integrated approach through engagement of scientific, administrative, and political communities across disciplines. There is ample evidence to suggest that modeling(More)
The disproportionate effects of the 2009 H1N1 pandemic on many Canadian Aboriginal communities have drawn attention to the vulnerability of these communities in terms of health outcomes in the face of emerging and reemerging infectious diseases. Exploring the particular challenges facing these communities is essential to improving public health planning. In(More)
  • Nick J. Pizzi
  • 2012 Annual Meeting of the North American Fuzzy…
  • 2012
Infectious disease modeling is a multi-disciplinary research activity that has made significant inroads over the last decade with respect to its acceptance as a valuable and practical tool for public health experts and decision makers. With the ability to deal with imprecise, approximate, and vague scenarios, soft computing can play an important role in(More)
The analysis of feature variance is a common approach used for data interpretation. In the case of pattern classification, however, the transformation of correlated features into a new set of uncorrelated variables must be used with caution, as there is no necessary causal connection between discriminatory power and variance. To compensate for this(More)
Classifying biomedical spectra is often difficult due to the bir voluminous nature; typically, only a small subset of spectral features is discriminatory, while the large majority tends to have a confounding effect on pattern classifiers. We present a two-pronged approach to dealing with this issue. First, we describe an iterative technique whereby many(More)
The automated prediction of qualitative attributes such as software complexity is a desirable software engineering goal. A potential technique is to use software metrics as quantitative predictors for these kinds of attributes. We describe a pattern classification method where a large collection of classifiers is presented with randomly selected subsets of(More)
While many techniques exist to classify data possessing straightforward characteristics, they tend to fail when dealing with the “curse of dimensionality”. This condition, in which the ratio of features to samples is very large, is prevalent in many complex, voluminous biomedical datasets acquired using current spectroscopic modalities. We(More)
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