Last data update: Sep 16, 2024. (Total: 47680 publications since 2009)
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Query Trace: Rolka HR [original query] |
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Comments on 'Some methodological issues in biosurveillance'
Rolka HR . Stat Med 2011 30 (5) 416-9 I would first like to compliment Dr Fricker on the outreaching, cross-disciplinary nature of this work. Underlying his discussion of several important methodological issues in biosurveillance is an important theme: successful methods and approaches from other fields of endeavor have much to offer for the analysis and exploitation of public health data. Dr Fricker focuses on three topics from which applied public health surveillance and biosurveillance can certainly benefit: techniques for measuring change from the field of statistical process control, examples from perception and cognition theory (e.g. the ‘looking for everything’ analogy to sydromic surveillance), and the value of using simulation for substantiating new methodological applications. His development of concepts remains rooted in the goals of early disease event detection in a population and extends to the less rigorously defined but more intuitive goal of acquiring and maintaining situational awareness. Dr Fricker also clearly acknowledges various disciplines and problem areas of research necessary for more successfully addressing these goals. |
Preface
Rolka HR . Stat Med 2010 30 (5) 401-2 The Twelfth Biennial Symposium on Statistical Methods was held in Decatur, Georgia April 6–8, 2009 and sponsored by the Centers for Disease Control and Prevention (CDC) and the American Statistical Association (ASA). The theme, ‘Info-fusion: Utilization of Multi-source Data’, was selected to focus attention on the exchange and use of data and information from diverse sources in extracting evidence for public health program and policy decisions. The success of the public health mission including, but not limited to, emergency preparedness and response is highly dependent on having a sound information supply chain. Maintaining adequate information and using it for good decisions transcends scientific disciplines. By themselves, information products support, but do not ensure, correct decisions; we generally have to take action with only partial information. Drawing conclusions using induction or the process of inference involves uncertainty that is generally best characterized using probability concepts and statistical reasoning. The characterization of uncertainty using probability is a common thread in the use of integrated data for decision support. This symposium was designed to draw from statistical and related information sciences across various areas of application, disciplines, and information technology concepts, which relate to integrating data and information for use in public health and included 230 participants. |
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