Accurate estimation of disease prevalence is crucial for effective public health intervention and resource allocation.Generating data by individual testing methods is often impractical and expensive for large populati...Accurate estimation of disease prevalence is crucial for effective public health intervention and resource allocation.Generating data by individual testing methods is often impractical and expensive for large populations,particularly when disease prevalence is low.Pool testing involves combining samples from multiple individuals into a pool and performing a single test,and offers a costeffective and efficient alternative.In pool testing strategy with retesting,if a pool tests negative,it is classified as non-defective,whereas if it is positive,then a retest is needed.The retesting strategy mitigates the effects of initial test errors,thereby enhancing the accuracy of the estimation of the prevalence rate.Evidence in the literature indicates that the traditional Wald method has been used to construct approximate confidence intervals for the prevalence rate.However,this interval estimation method is based on the normality ap-proximation and hence may not be accurate when the true prevalence rate is close to zero.In this paper,we propose a Bayesian interval estimation ap-proach which is not affected by extreme values of the prevalence rate and al-lows for incorporating prior information about the prevalence rate.We as-sumed that the prior distribution for the unknown prevalence rate p is a Beta distribution with parameters α_(0) and β_(0) and based on pool testing outcomes for the n pools each of size k,100(1-α)% credible intervals were constructed from the resulting posterior distribution.Simulation studies were carried out to compare the efficiencies of the Bayesian and Wald interval estimation methods for various values of p.展开更多
文摘Accurate estimation of disease prevalence is crucial for effective public health intervention and resource allocation.Generating data by individual testing methods is often impractical and expensive for large populations,particularly when disease prevalence is low.Pool testing involves combining samples from multiple individuals into a pool and performing a single test,and offers a costeffective and efficient alternative.In pool testing strategy with retesting,if a pool tests negative,it is classified as non-defective,whereas if it is positive,then a retest is needed.The retesting strategy mitigates the effects of initial test errors,thereby enhancing the accuracy of the estimation of the prevalence rate.Evidence in the literature indicates that the traditional Wald method has been used to construct approximate confidence intervals for the prevalence rate.However,this interval estimation method is based on the normality ap-proximation and hence may not be accurate when the true prevalence rate is close to zero.In this paper,we propose a Bayesian interval estimation ap-proach which is not affected by extreme values of the prevalence rate and al-lows for incorporating prior information about the prevalence rate.We as-sumed that the prior distribution for the unknown prevalence rate p is a Beta distribution with parameters α_(0) and β_(0) and based on pool testing outcomes for the n pools each of size k,100(1-α)% credible intervals were constructed from the resulting posterior distribution.Simulation studies were carried out to compare the efficiencies of the Bayesian and Wald interval estimation methods for various values of p.
