Coverart for item
The Resource Introduction to statistical methods for biosurveillance : with an emphasis on syndromic surveillance, Ronald D. Fricker

Introduction to statistical methods for biosurveillance : with an emphasis on syndromic surveillance, Ronald D. Fricker

Label
Introduction to statistical methods for biosurveillance : with an emphasis on syndromic surveillance
Title
Introduction to statistical methods for biosurveillance
Title remainder
with an emphasis on syndromic surveillance
Statement of responsibility
Ronald D. Fricker
Creator
Contributor
Subject
Genre
Language
eng
Cataloging source
NhCcYBP
Dewey number
614.4
Index
no index present
LC call number
RA652.2.P82
LC item number
F75 2013
Literary form
non fiction
Nature of contents
dictionaries
Label
Introduction to statistical methods for biosurveillance : with an emphasis on syndromic surveillance, Ronald D. Fricker
Link
http://site.ebrary.com/lib/byuprovo/docDetail.action?docID=10659337
Instantiates
Publication
Note
4.2.3 Graphical Methods for Spatial and Spatio-temporal Data
Carrier category
online resource
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Contents
  • Hotelling's 2 Detection Method Tables; Preface; Additional Reading; Additional Reading; Union of Disjoint Events; Acknowledgments; I Introduction to Biosurveillance; 1 Overview; Chapter Objectives; Chapter Objectives; Chapter Objectives; Chapter Objective; Mathematical Notation; 1.1 What Is Biosurveillance?; 1.1.1 Biosurveillance Objectives; 1.2 Biosurveillance Systems; 1.2.1 Components; 1.2.2 Examples; 1.3 Biosurveillance Utility and Effectiveness; 1.3.1 Biosurveillance Compared with Traditional Public Health Surveillance
  • 1.3.2 Biosurveillance Early Event Detection Compared with Statistical Process Control1.4 Discussion and Summary; Mathematical Notation; Mathematical Notation; 2 Biosurveillance Data; Chapter Objectives; Mathematical Notation; Chapter Objectives; Mathematical Notation; Chapter Objectives; Mathematical Notation; Chapter Objectives; Mathematical Notation; Chapter Objectives; Mathematical Notation; Chapter Objective; Mathematical Notation; Mathematical Notation; 2.1 Types of Data; 2.2 Types of Biosurveillance Data; 2.2.1 Additional Characteristics of Biosurveillance Data
  • 2.2.2 Syndromic Surveillance DataExample: County-Level Clinic Data; Bar Chart; Additional Reading; Description; Comparisons; Modeling Distributional Parameters; Multivariate Cumulative Sum Detection Method Tables; Example: City-Level Hospital Data; Histogram; Performance Comparisons; Additional Reading; Modeling Outbreaks; 2.3 Data Preparation; 2.3.1 Cleaning; 2.3.2 Coding; 2.3.3 Imputing; Mean Value Imputation; Lattice (or Trellis) Plots; Description; Injecting Outbreaks in Actual Data; Regression Imputation; Box Plot; BioSense Variants; Explicitly Modeling Data Features; Hot Deck Imputation
  • ScatterplotPerformance Comparisons; Modeling Distributional Parameters; Multiple Imputation; 2.4 Discussion and Summary; Time Series Plots; Additional Reading; Aggregate-Level Spatio-temporal Data Simulations; Additional Reading; Repeated Cross-sectional Plots; Individual-Level Spatio-temporal Data Simulations; II Situational Awareness; 3 Situational Awareness for Biosurveillance; 3.1 What Is Situational Awareness?; 3.2 A Theoretical Situational Awareness Model; 3.3 Biosurveillance Situational Awareness; 3.4 Extending the Situational Awareness Model: Situated Cognition
  • 3.5 Discussion and Summary4 Descriptive Statistics for Comprehending the Situation; 4.1 Numerical Descriptive Statistics; 4.1.1 Descriptive Statistics for Cross-sectional Data; 4.1.1.1 Measures of Central Tendency; 4.1.1.2 Measures of Variation; 4.1.1.3 Other Numerical Summary Statistics; 4.1.1.4 Measures of How Two Variables Co-vary; 4.1.2 Descriptive Statistics for Longitudinal Data; 4.1.2.1 Statistics from Moving Windows of Data; 4.1.2.2 Autocorrelation; 4.2 Graphical Descriptive Statistics; 4.2.1 Graphical Methods for Cross-sectional Data; 4.2.2 Graphical Methods for Longitudinal Data
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9781107334502
Media category
computer
Media MARC source
rdamedia
Reproduction note
Electronic reproduction.
Specific material designation
remote
Label
Introduction to statistical methods for biosurveillance : with an emphasis on syndromic surveillance, Ronald D. Fricker
Link
http://site.ebrary.com/lib/byuprovo/docDetail.action?docID=10659337
Publication
Note
4.2.3 Graphical Methods for Spatial and Spatio-temporal Data
Carrier category
online resource
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Contents
  • Hotelling's 2 Detection Method Tables; Preface; Additional Reading; Additional Reading; Union of Disjoint Events; Acknowledgments; I Introduction to Biosurveillance; 1 Overview; Chapter Objectives; Chapter Objectives; Chapter Objectives; Chapter Objective; Mathematical Notation; 1.1 What Is Biosurveillance?; 1.1.1 Biosurveillance Objectives; 1.2 Biosurveillance Systems; 1.2.1 Components; 1.2.2 Examples; 1.3 Biosurveillance Utility and Effectiveness; 1.3.1 Biosurveillance Compared with Traditional Public Health Surveillance
  • 1.3.2 Biosurveillance Early Event Detection Compared with Statistical Process Control1.4 Discussion and Summary; Mathematical Notation; Mathematical Notation; 2 Biosurveillance Data; Chapter Objectives; Mathematical Notation; Chapter Objectives; Mathematical Notation; Chapter Objectives; Mathematical Notation; Chapter Objectives; Mathematical Notation; Chapter Objectives; Mathematical Notation; Chapter Objective; Mathematical Notation; Mathematical Notation; 2.1 Types of Data; 2.2 Types of Biosurveillance Data; 2.2.1 Additional Characteristics of Biosurveillance Data
  • 2.2.2 Syndromic Surveillance DataExample: County-Level Clinic Data; Bar Chart; Additional Reading; Description; Comparisons; Modeling Distributional Parameters; Multivariate Cumulative Sum Detection Method Tables; Example: City-Level Hospital Data; Histogram; Performance Comparisons; Additional Reading; Modeling Outbreaks; 2.3 Data Preparation; 2.3.1 Cleaning; 2.3.2 Coding; 2.3.3 Imputing; Mean Value Imputation; Lattice (or Trellis) Plots; Description; Injecting Outbreaks in Actual Data; Regression Imputation; Box Plot; BioSense Variants; Explicitly Modeling Data Features; Hot Deck Imputation
  • ScatterplotPerformance Comparisons; Modeling Distributional Parameters; Multiple Imputation; 2.4 Discussion and Summary; Time Series Plots; Additional Reading; Aggregate-Level Spatio-temporal Data Simulations; Additional Reading; Repeated Cross-sectional Plots; Individual-Level Spatio-temporal Data Simulations; II Situational Awareness; 3 Situational Awareness for Biosurveillance; 3.1 What Is Situational Awareness?; 3.2 A Theoretical Situational Awareness Model; 3.3 Biosurveillance Situational Awareness; 3.4 Extending the Situational Awareness Model: Situated Cognition
  • 3.5 Discussion and Summary4 Descriptive Statistics for Comprehending the Situation; 4.1 Numerical Descriptive Statistics; 4.1.1 Descriptive Statistics for Cross-sectional Data; 4.1.1.1 Measures of Central Tendency; 4.1.1.2 Measures of Variation; 4.1.1.3 Other Numerical Summary Statistics; 4.1.1.4 Measures of How Two Variables Co-vary; 4.1.2 Descriptive Statistics for Longitudinal Data; 4.1.2.1 Statistics from Moving Windows of Data; 4.1.2.2 Autocorrelation; 4.2 Graphical Descriptive Statistics; 4.2.1 Graphical Methods for Cross-sectional Data; 4.2.2 Graphical Methods for Longitudinal Data
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9781107334502
Media category
computer
Media MARC source
rdamedia
Reproduction note
Electronic reproduction.
Specific material designation
remote

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