Presents basic and advanced methods with a focus on demonstrated added value for a broad class of public health surveillance problems.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Ronald D. Fricker, Jr is an Associate Professor of Operations Research and the Associate Chair for Research at the Naval Postgraduate School (NPS). Prior to joining NPS, Dr Fricker was a Senior Statistician at the RAND Corporation and the Associate Director of the National Security Research Division. Published widely in leading professional journals, he is a Fellow of the American Statistical Association, an Elected Member of the International Statistical Institute, and a former chair of the ASA Section on Statistics in Defense and National Security. He is a contributing editor to Interfaces and is on the editorial boards of Statistics, Politics, and Policy and the International Journal of Quality Engineering and Technology. Fricker's current research is focused on studying the performance of various statistical methods for use in biosurveillance, particularly syndromic surveillance, and statistical process control methodologies more generally.
Inhaltsangabe
Part I. Introduction to Biosurveillance: 1. Overview 2. Biosurveillance data Part II. Situational Awareness: 3. Situational awareness for biosurveillance 4. Descriptive statistics for displaying the situation 5. Statistical models for evaluating the situation Part III. Early Event Detection: 6. Design and performance evaluation 7. Univariate temporal methods 8. Multivariate temporal methods 9. Spatio-temporal methods Part IV. Putting It All Together: 10. Simulating biosurveillance data 11. Applying the temporal methods to real data 12. Comparing methods to better understand and improve 13. Frontiers, open questions, and future research.
Part I. Introduction to Biosurveillance: 1. Overview 2. Biosurveillance data Part II. Situational Awareness: 3. Situational awareness for biosurveillance 4. Descriptive statistics for displaying the situation 5. Statistical models for evaluating the situation Part III. Early Event Detection: 6. Design and performance evaluation 7. Univariate temporal methods 8. Multivariate temporal methods 9. Spatio-temporal methods Part IV. Putting It All Together: 10. Simulating biosurveillance data 11. Applying the temporal methods to real data 12. Comparing methods to better understand and improve 13. Frontiers, open questions, and future research.
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