APPLICATION OF HIERARCHICAL BAYESIAN MODELS FOR MODELING ECONOMIC COSTS IN THE IMPLEMENTATION OF NEW DIAGNOSTIC TESTS

Abstract

The COVID-19 pandemic has highlighted the need for reliable and rapid diagnostic tests to control the spread of infection. The introduction of new rapid antigen tests often goes in tandem with limited data availability, making it challenging to assess their performance in the initial phase of the pandemic. Sensitivity and specificity, the key performance characteristics provided by manufacturers, are typically derived under laboratory conditions and may not accurately reflect the tests' performance in field settings. We use a hierarchical Bayesian model to obtain realistic estimates under real-world conditions and show how it may be used in situations in which new tests with limited history are presented on the market. The proposed methodology allows for efficient information pooling, thereby improving the accuracy of parameter estimates for new tests. The results suggest that the application of the hierarchical model to the Czech data led to a considerable reduction in uncertainty associated with the parameter estimates, as well as with the potential economic cost implied by false positive test results. The model can thus assist in better informed decision-making and financial planning for both the government and corporations.

Data:
Received: 6 Sep 2024
Revised: 20 Oct 2024
Accepted: 1 Nov 2024
Published: 15 Nov 2024