Surveillance System Analysis
Surveillance System Analysis
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Contents

(Freedom) FreedomTrail

Table of Contents

  1. Title Page
  2. Introduction
  3. List of abbreviations
  4. List of Tables
  5. List of Figures
  6. List of Formulae
  7. Fundamentals and terminology
    1. Main concept
    2. Theory of freedom
      1. Condition of interest
      2. Measures of disease occurrence
      3. What we mean by free
    3. Probability and distributions
      1. Prevalence
      2. Quality of estimates
      3. Conditional probability
      4. Bayes theorem
      5. Probability density function
        1. PDF of a dichotomous variable
        2. PDF of a binomial variable
    4. Tests, sensitivity and specificity
      1. Diagnostic test
        1. Evaluation of diagnostic tests
        2. Sensitivity and Specificity
        3. Variance of accuracy measures
        4. Apparent prevalence
        5. Operational diagnostic test parameters
      2. Testing systems
      3. Multiple diagnostic tests
      4. Herd testing
    5. Bayes theorem and predictive values
      1. Positive predictive value
      2. Negative predictive value
      3. The link between pre- and post-test probability of disease
      4. Application of predictive values in disease surveillance
    6. Design prevalence
      1. Definition and legal issues
      2. Biological issues
      3. Statistical issues and application
    7. Describing differential risk
    8. Internal time period for analysis
      1. Surveys
      2. Ongoing surveillance
      3. Statistical evidence from surveys and surveillance
    9. Documenting evidence for disease freedom in small herds
    10. Bayesian inference
      1. The beta prior for proportions
      2. How to get the prior?
      3. Obtaining the posterior
      4. Markov chain Monte Carlo
  8. Surveillance, Monitoring and Surveys
    1. Data collection for Surveillance
    2. Targeted Surveillance
    3. The impact of the change in trade regulations on surveillance planning and implementation
  9. Methodological approach
    1. Identifying potential data sources
      1. Drawing up a scenario tree
      2. Estimating branch probabilities and proportions
      3. Analysing results of application of the SSC
      4. Simulated data
    2. Describing surveillance system components
      1. Scope of the model
      2. Unit of analysis
      3. Coverage
      4. Accounting for lack of coverage
      5. Temporal applicability
      6. Outcomes
      7. Specificity
    3. Development of scenario trees
      1. Functions of a scenario tree in analysis of a SSC
      2. Node types
        1. Infection nodes
        2. Detection nodes
        3. Category nodes
        4. Risk category nodes
        5. Detection category nodes
      3. Grouping levels
      4. Ordering of nodes
      5. Factors to include
      6. Estimation of branch probabilities
        1. Using available data
        2. Expert opinion
      7. Design Prevalence
        1. Determining the design prevalence
        2. Small herds
      8. Incorporating differential risk
        1. Multiple risk nodes
      9. Building of scenario trees
      10. Root node
      11. Completion and truncation
    4. Analysing the tree
      1. Analysing the tree assuming independence
      2. Advanced approach - accounting for lack of independence among units
      3. Calculation of unit sensitivity
      4. Stepwise calculation of system sensitivity
        1. Calculation of group level sensitivity
          1. Binomial approach
          2. Hypergeometric approach
          3. Exact method
        2. Calculating sensitivity at higher grouping levels
          1. Binomial
          2. Hypergeometric
          3. Exact
      5. Approaches to calculating component sensitivity
      6. Calculating Country-level sensitivity
      7. Category proportions
    5. Sensitivity ratio
    6. Combination of data from multiple sources
      1. Accounting for lack of independence
      2. Incorporating data from representative surveys
    7. Calculation of the probability of country freedom
      1. Selection of a prior
      2. Temporal discounting of historical surveillance data
        1. Introduction
        2. Method
        3. Probability of disease introduction - interface with IRA?
        4. Acceptable Level of Protection
    8. Stochastic modelling in the context of disease freedom
      1. Stochastic modelling in the context of disease freedom - Principles
      2. Practical implementation
    9. Data from multiple sources
      1. Sampling probability on animal level
      2. Sampling probability on herd level and for population strata
      3. Base-line dependence among surveillance components
      4. Dependence among diagnostic methods
      5. Estimating the surveillance system's sensitivity under dependence
    10. Current issues
      1. Independence of tests
      2. Independence of surveillance system components
      3. Eliciting and combining expert opinion
      4. Non-quantitative analyses
  10. References & Bibliography
  11. Project Credits
  12. Appendices
    1. Suggested notation
      1. Variable names
      2. Design prevalence
      3. Sensitivity notation
      4. Probabilities
      5. Branch proportions
      6. Risk nodes
    2. Stochastic modelling using @Risk
      1. For those not familiar with @Risk
      2. Inputs and Outputs
      3. Distribution functions
      4. @Risk statistics functions
      5. Simulation files
      6. Sensitivity analysis
      7. Correlated inputs
    3. Using PopTools for stochastic spreadsheet simulation
    4. Australian Biosecurity CRC
    5. International EpiLab in Denmark
      1. Projects of the International EpiLab related to disease freedom
        1. International EpiLab Project 3 (CSF; Cameron)
        2. International EpiLab Project 4 (HPAI; Martin)
        3. International EpiLab Project 5 (IBR; Salman)

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Page last modified on September 18, 2008, at 03:55 PM by jenny