# Misclassification errors in prevalence estimation: Bayesian handling with care

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This Hints and Kinks paper starts from the simple but well-known premise that “what gets measured, gets done”, which we would like to extend into “what gets measured well, gets done well”, and finally to “what does not get measured well could still get done well, if appropriate analytical methods are used”.

Imagine assessing the prevalence of an infectious disease in a population, where the presence of disease is determined by a diagnostic test. For each tested individual, the diagnostic test result gives a “signal” that does not necessarily match its true infection status. It is well known that false positive and false negative results can arise when using diagnostic tests, for example producing a positive result in a non-case owing to a factor unrelated to the infection. On a population level, the prevalence as determined by the diagnostic test will thus only be an “apparent” prevalence, which will, to some extent, differ from the “true” prevalence. This problem of diagnostic test...

## Keywords

Infection Status Malaria Prevalence Test Characteristic External Information Positive Test Result## References

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