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El. knyga: Introduction to Statistical Methods for Biosurveillance: With an Emphasis on Syndromic Surveillance

(Naval Postgraduate School, Monterey, California)
  • Formatas: PDF+DRM
  • Išleidimo metai: 25-Feb-2013
  • Leidėjas: Cambridge University Press
  • Kalba: eng
  • ISBN-13: 9781107330559
  • Formatas: PDF+DRM
  • Išleidimo metai: 25-Feb-2013
  • Leidėjas: Cambridge University Press
  • Kalba: eng
  • ISBN-13: 9781107330559

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"While the public health philosophy of the 20th Century -- emphasizing prevention -- is ideal for addressing natural disease outbreaks, it is not sufficient to confront 21st Century threats where adversaries may use biological weapons agents as part of along-term campaign of aggression and terror. Health care providers and public health officers are among our first lines of defense. Therefore, we are building on the progress of the past three years to further improve the preparedness of our public health and medical systems to address current and future BW [ biological warfare] threats and to respond with greater speed and flexibility to multiple or repetitive attacks." Homeland Security Presidential Directive 21 Bioterrorism is not a new threat in the 21st century -- thousands of years ago the plague and other contagious diseases were used in warfare -- but today the potential for catastrophic outcomes is greater than it has ever been. To address this threat, the medical and public health communities are putting various measures in place, including systems designed to pro-actively mon- itor populations for possible disease outbreaks"--Provided by publisher.

Daugiau informacijos

Presents basic and advanced methods with a focus on demonstrated added value for a broad class of public health surveillance problems.
Preface xi
Acknowledgments xv
Part I Introduction to Biosurveillance
1 Overview
3(20)
1.1 What Is Biosurveillance?
5(5)
1.2 Biosurveillance Systems
10(5)
1.3 Biosurveillance Utility and Effectiveness
15(5)
1.4 Discussion and Summary
20(3)
2 Biosurveillance Data
23(32)
2.1 Types of Data
25(1)
2.2 Types of Biosurveillance Data
26(11)
2.3 Data Preparation
37(13)
2.4 Discussion and Summary
50(5)
Part II Situational Awareness
3 Situational Awareness for Biosurveillance
55(12)
3.1 What Is Situational Awareness?
57(1)
3.2 A Theoretical Situational Awareness Model
57(3)
3.3 Biosurveillance Situational Awareness
60(1)
3.4 Extending the Situational Awareness Model: Situated Cognition
61(3)
3.5 Discussion and Summary
64(3)
4 Descriptive Statistics for Comprehending the Situation
67(44)
4.1 Numerical Descriptive Statistics
70(14)
4.2 Graphical Descriptive Statistics
84(23)
4.3 Discussion and Summary
107(4)
5 Statistical Models for Projecting the Situation
111(38)
5.1 Modeling Time Series Data
114(4)
5.2 Smoothing Models
118(11)
5.3 Regression-Based Models
129(9)
5.4 ARMA and ARIMA Models
138(3)
5.5 Change Point Analysis
141(4)
5.6 Discussion and Summary
145(4)
Part III Early Event Detection
6 Early Event Detection Design and Performance Evaluation
149(29)
6.1 Notation and Assumptions
152(2)
6.2 Design Points and Principles
154(3)
6.3 Early Event Detection Methods Differ from Other Statistical Tests
157(9)
6.4 Measuring Early Event Detection Performance
166(9)
6.5 Discussion and Summary
175(3)
7 Univariate Temporal Methods
178(40)
7.1 Historical Limits Detection Method
182(1)
7.2 Shewhart Detection Method
183(9)
7.3 Cumulative Sum Detection Method
192(11)
7.4 Exponentially Weighted Moving Average Detection Method
203(9)
7.5 Other Methods
212(3)
7.6 Discussion and Summary
215(3)
8 Multivariate Temporal and Spatio-temporal Methods
218(35)
8.1 Multivariate Temporal Methods
221(21)
8.2 Spatio-temporal Methods
242(6)
8.3 Discussion and Summary
248(5)
Part IV Putting It All Together
9 Applying the Temporal Methods to Real Data
253(28)
9.1 Using Early Event Detection Methods to Detect Outbreaks and Attacks
257(11)
9.2 Assessing How Syndrome Definitions Affect Early Event Detection Performance
268(11)
9.3 Discussion and Summary
279(2)
10 Comparing Methods to Better Understand and Improve Biosurveillance Performance
281(24)
10.1 Performance Comparisons: A Univariate Example
285(10)
10.2 Performance Comparisons: A Multivariate Example
295(6)
10.3 Discussion and Summary
301(4)
Part V Appendices
A A Brief Review of Probability, Random Variables, and Some Important Distributions
305(30)
A.1 Probability
308(5)
A.2 Random Variables
313(5)
A.3 Some Important Probability Distributions
318(17)
B Simulating Biosurveillance Data
335(31)
B.1 Types of Simulation
337(6)
B.2 Simulating Biosurveillance Data
343(21)
B.3 Discussion and Summary
364(2)
C Tables
366(15)
References 381(10)
Author Index 391(4)
Subject Index 395
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.