The probability of failing in detecting an infectious disease at entry points into a country

Area of Science:

• Epidemiology
• Biostatistics
• Infectious Disease Control

Background:

• Accurate diagnostic testing is crucial for disease surveillance and control.
• Understanding the probability of infection detection failure is essential for public health interventions.
• Severe Acute Respiratory Syndrome (SARS) highlighted the need for effective disease importation prevention strategies.

Purpose of the Study:

• To evaluate the probability of failing to detect an infection in a population sample.
• To determine the minimum sample size needed for a desired detection failure probability.
• To provide confidence intervals for infection prevalence based on negative test results.

Main Methods:

• Utilizing hypergeometric distribution to calculate exact probabilities of infection detection failure.
• Determining sample size as a function of infection prevalence (pi).
• Applying frequentistic and Bayesian approaches for confidence interval estimation.

Main Results:

• The probability of failing to detect an infection depends on sample size and prevalence.
• Calculations provide the minimum number of individuals to test for a desired low probability of detection failure.
• Confidence intervals for infection prevalence were derived for scenarios with all negative test results.

Conclusions:

• Entry screening using diagnostic tests is generally ineffective for preventing disease importation.
• The study's findings have implications for public health policy regarding border screening and disease surveillance.
• Accurate assessment of diagnostic test performance and sampling strategies is critical for effective disease control.