|
Abstract:
|
Reducing the cost of testing is an important consideration in disease surveillance programs. When the prevalence of disease is small, group testing (pooled testing) serves as an excellent cost-effective approach for the surveillance of infectious diseases. Over the years, group testing has been broadly studied for estimating the prevalence of single diseases, such as HIV. When multiple diseases are concerned, group testing has been explored only for basic scenarios, such as one- and two-stage hierarchical algorithms. The aim of our work is to overcome these limitations and to provide a general framework that can model multiplex pooling data with any number of stages. We study the efficiency and cost-efficiency of the likelihood-based estimators involving two or more diseases and identify the optimal pooling configurations, such as the optimal pool size and the optimal number of stages. The research outcomes will be useful in the design stage of group testing applications. Our work will be illustrated by simulation as well as by a chlamydia and gonorrhea data application. Our work will provide an R function in order to make the application easier for the practitioners.
|
