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Risk-based surveillance

Complex 2-stage risk-based surveillance - calculation of surveillance sensitivity based on herd testing data

Herd-level risk factor

Animal-level risk factor

Diagnostic test


Paste herd testing data in the space below. Include only one row per herd sampled. Data columns can be in any order but must include columns for the number of high-risk animals tested (labeled 'HRTested'), the number of low-risk animals tested (labeled 'LRTested') and herd risk-group (labeled 'HerdRisk'). 'HerdRisk' must be coded as high-risk = 1 and low-risk = 0. Columns for herd id and proportion of the herd that is high-risk (labeled 'HRProportion') are optional. A header row specifying column names must also be included.

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This page calculates the surveillance sensitivity for complex risk-based surveillance with 2-stage sampling, based on actual herd-testing data. For example, a survey in which high risk herds are preferentially targeted for testing, and high-risk animals preferentially targeted within selected herds. In addition, different test sensitivities can be applied to high-risk and low-risk animals in each sampled herd (i.e. sensitivity is based on risk-group). If test sensitivity varies between groups but there is no difference in risk of disease use a relative risk value of 1. The analysis is based on actual sampling results for each herd, assuming that all results are negative.

The analysis assumes 2-stage sampling to account for clustering of disease (for example at the herd, flock or village level) and that the effective specificity of the surveillance system is equal to one (all positives are followed up to ensure that they are not false positives):

Two risk factors are considered, one at the herd level and one at the animal level. For each risk factor the following information is required:

  • The relative risk: this measures the risk of herds (or animals) in the high-risk group being infected, relative to the risk of herds (animals) in the low-risk group being infected. For risk-based surveillance, this should usually be greater than 1; and
  • The population proportion: this is the proportion of herds (or animals) from their respective populations that are in the high-risk group. Herd-specific values will be used where available, or the population value entered here will be used where herd-specific values are not available.

In addition, the following parameters are required:

  • The design prevalence at the herd and animal levels: this is the assumed prevalence of disease (proportion of infected herds and infected animals respectively), if the disease is present in the population. It is used as a standard by which the sensitivity of the surveillance can be evaluated;
  • For the diagnostic test, performance depends on risk group:
    •   . The sensitivity of the test for animals in the high risk group; and
    •   . The sensitivity of the test for animals in the low risk group.
  • Prior confidence of freedom: this is the estimated confidence that the population was free of disease (at the design prevalence) before the surveillance was done and is used to estimate confidence of freedom following completion of the surveillance; and
  • Detailed data on the number of animals tested in each herd, including: herd-risk group, number of high-risk animals tested, number of low-risk animals tested, herd id (optional) and herd-specific population proportion of high-risk animals (optional). Include only one row of data per herd sampled.

Outputs include:


  • The sensitivity of the surveillance system, or in other words, the probability that the surveillance system would detect at least one infected animal if disease was present at the specified design prevalence;
  • The probability of freedom, or confidence that the disease is NOT present at the design prevalence (equivalent to the negative predictive value of the surveillance system);
  • The sensitivity of the system and confidence of freedom for assumed representative sampling;
  • The sensitivity ratio - this indicates how much more sensitivity the risk-based approach achieves, relative to a representative approach;
  • Effective probabilities of infection (EPI) for high-risk and low-risk herds (and for high-risk and low-risk animals in each herd). EPI values approaching 100% suggest that, based on the values used for relative risk, population proportions and design prevalence, close to 100% of herds (or animals) in the high-risk group are expected to be infected. If this is unreasonable you may need to review the input values. Values over 100% mean that the model is invalid and processing will be stopped, with an error message. Input values must be changed to ensure EPI values are appropriate;
  • Numbers of high-risk and low-risk herds tested and the total;
  • A summary of herd-sensitivities achieved in tested herds for risk-based and representative sampling; and
  • Risk-based and representative herd-sensitivities and sensitivity ratio achieved in individual herds.

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1-sample t-test for summary data
1-sample z-test for a population proportion
1-Stage Freedom analysis
2-sample t-test for summary data
2-sample z-test to compare sample proportion
2-Stage surveys for demonstration of freedom
Analyse test repeatability
Analyse two-stage prevalence data
Analysis of 2-stage freedom survey data
Analysis of simple 2-stage freedom survey
Bioequivalence analysis - two-period, two-treatment crossover trial
Calculate Cluster-level sensitivity and specificity for range of sample sizes and cut-points for given cluster size and imperfect tests
Calculate confidence limits for a sample proportion
Calculate sample sizes for 2-stage freedom survey where individual cluster details are available
Calculate sample sizes for 2-stage freedom survey where individual cluster details are NOT available
Calculate sample sizes for 2-stage freedom survey with fixed cluster-level sensitivity
Calculate test Sensitivity and Specificity and ROC curves
Capture-Recapture analysis
Chi-squared test for contingency table from original data
Chi-squared test for homogeneity of a sample
Chi-squared test for r x c contingency table
Chi-squared test for trend
Cluster-level sensitivity and specificity with variable cut-points
Compare prevalence values
Compare two tests
Complex 2-stage risk-based surveillance - calculation of surveillance sample size
Complex 2-stage risk-based surveillance - calculation of surveillance sensitivity
Complex 2-stage risk-based surveillance - calculation of surveillance sensitivity based on herd testing data
Complex risk-based surveillance - calculation of surveillance sample size
Complex risk-based surveillance - calculation of surveillance sensitivity
Confidence of population freedom (NPV) for a surveillance system
Confidence of population freedom for multiple time periods
Contact
Design prevalence required to achieve target population (cluster or system) sensitivity
Diagnostic test evaluation and comparison
Estimate 95% confidence limits for a median
Estimate alpha and beta Parameters for Beta distributions from count data
Estimate confidence limits for a mean
Estimate parameters for multiple Beta probability distributions or summarise distributions for specified parameters
Estimated true prevalence and predictive values from survey testing
Estimated true prevalence using one test with a Gibbs sampler
Estimated true prevalence using two tests with a Gibbs sampler
Estimating prevalence
Estimation of alpha and beta parameters for prior Beta distributions
"EUFMD - Demonstration of FMD freedom": 2-stage risk-based surveillance with 1 herd-level risk factor, 1 animal-level risk factor and multiple surveillance components
FreeCalc: Analyse results of freedom testing
FreeCalc: Calculate sample size for freedom testing with imperfect tests
Get P and critical values for the Chi-squared distribution
Get P and critical values for the F distribution
Get P and critical values for the normal distribution
Get P and critical values for the t distribution
Glossary
HerdPlus utilities
HerdPlus: Calculate SeH and SpH for a single herd
HerdPlus: SeH and SpH comparison for varying herd sizes
HerdPlus: SeH and SpH for listed herd sizes and optimised sample sizes
HerdPlus: SeH and SpH for optimised sample sizes for range of herd sizes
HerdPlus: SeH and SpH for range of sample sizes and cut-points for given herd size
HerdPlus: SeH and SpH for varying sample sizes
HerdPlus: SeH for fixed sample size and cut-point
HerdPlus: SeH for optimised sampling strategy
HerdPlus: SeH for varying design prevalence
Home
Likelihood ratios and probability of infection in a tested individual
Mantel-Haenszel chi-square test for stratified 2 by 2 tables
McNemar's chi-squared test for association of paired counts
Numbers of false positives to a test
One-sample test to compare sample mean or median to population estimate
Paired t-test or Wilcoxon signed rank test on numeric data
Pooled Prevalence
Pooled Prevalence Calculator - Demonstration analyses
Pooled Prevalence Calculator - Demonstration analyses - 1
Pooled Prevalence Calculator - Demonstration analyses - 2
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Pooled Prevalence Calculator - Demonstration analyses - 8
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Pooled Prevalence Calculator - Demonstration analyses - 11
Pooled Prevalence Calculator - Demonstration analyses - 12
Pooled Prevalence Calculator - Demonstration analyses - 13
Pooled Prevalence Calculator - Demonstration analyses - 14
Pooled Prevalence Calculator - Demonstration analyses - 15
Pooled Prevalence Calculator - Demonstration analyses - 16
Pooled Prevalence Calculator - Demonstration analyses - 17
Pooled prevalence for fixed pool size and perfect tests
Pooled prevalence for fixed pool size and tests with known sensitivity and specificity
Pooled prevalence for fixed pool size and tests with uncertain sensitivity and specificity
Pooled prevalence for variable pool size and perfect tests
Pooled prevalence using a Gibbs sampler
Population (or cluster) sensitivity for varying unit sensitivity
Population level (or herd, flock, cluster, or other grouping) sensitivity
Population or cluster level sensitivity using pooled sampling
Positive and Negative Predictive Values for a test
Probability of infection in a test-negative sample
Random Geographic Coordinates Sampling
Random Number Sampling
Random sampling from a sampling frame
Random sampling from populations
Random sampling of animals
References
Risk-based surveillance
Sample size calculation for fixed pool size and perfect tests
Sample size calculation for fixed pool size and uncertain sensitivity and specificity
Sample size calculations
Sample size for a case-control study
Sample size for a cohort study
Sample size for demonstration of freedom (detection of disease) using pooled testing
Sample Size for survival analysis to compare median times since last outbreak
Sample size required to achieve target confidence of freedom
Sample size to achieve specified population level (or herd, flock, cluster, etc) sensitivity
Sample size to detect a significant difference between 2 means with equal sample sizes and variances
Sample size to detect a significant difference between 2 means with unequal sample sizes and variances
Sample size to detect a significant difference between 2 proportions
Sample size to estimate a proportion or apparent prevalence with specified precision
Sample size to estimate a single mean with specified precision
Sample size to estimate a true prevalence with an imperfect test
Sample size to estimate a true prevalence with an imperfect test
Simple 2-stage risk-based surveillance - calculation of sample size
Simple 2-stage risk-based surveillance - calculation of surveillance sensitivity
Simple 2-stage risk-based surveillance - calculation of surveillance sensitivity based on herd testing data
Simple risk-based surveillance - calculation of minimum detectable prevalence
Simple risk-based surveillance - calculation of sample size
Simple risk-based surveillance - calculation of surveillance sensitivity
Simple risk-based surveillance with differential sensitivity - calculation of sample size with two sensitivity groups
Simple risk-based surveillance with differential sensitivity - calculation of surveillance sensitivity
Simulate sampling for fixed pool size and assumed known test sensitivity and specificity
Simulate sampling for fixed pool size and assumed perfect test
Simulate sampling for fixed pool size and uncertain test sensitivity and specificity
Simulate sampling for variable pool sizes
Simulated true prevalence estimates from survey testing with an imperfect test
Statistical analysis of numeric data
Stochastic analysis of 2-stage freedom survey data
Summarise Beta probability distributions for specified alpha and beta parameters
Summarise Binomial probability distributions for specified sample size and probability
Summarise categorical or continuous data
Summarise continuous data (ungrouped)
Summarise continuous data by single grouping variable
Summarise measures of association from a 2x2 table
Summarise Pert probability distributions for specified minimum, mode and maximum values
Summarise probability distributions
Survey Toolbox for livestock diseases
Survival analysis of herd incidence data
Test evaluation against a gold standard
User guide - Home
User guide 1 - Introduction
User guide 2 - Overview
User guide 3 - Bayesian vs frequentist methods
User guide 4 - Pooled prevalence for fixed pool size and perfect tests
User guide 5 - Pooled prevalence for fixed pool size and tests with known sensitivity and specificity
User guide 6 - Pooled prevalence for fixed pool size and tests with uncertain sensitivity and specificity
User guide 7 - Pooled prevalence for variable pool size and perfect tests
User guide 8 - Pooled prevalence using a Gibbs sampler
User guide 9 - Estimated true prevalence using one test with a Gibbs sampler
User guide 10 - Estimated true prevalence using two tests with a Gibbs sampler
User guide 11 - Estimation of alpha and beta parameters for prior Beta distributions and summarisation of Beta distributions for specified alpha and beta parameters
User guide 12 - Sample size for fixed pool size and perfect test
User guide 13 - Sample size for fixed pool size and known test sensitivity and specificity
User guide 14 - Sample size for fixed pool size and uncertain test sensitivity and specificity
User guide 15 - Simulate sampling for fixed pool size
User guide 16 - Simulate sampling for variable pool sizes
User guide 17 - Important Assumptions
User guide 18 - Pooled prevalence estimates are biased!