Application of Hierarchical Bayesian Models for Modeling Economic Costs in the Implementation of New Diagnostic Tests

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 the limited data availability, making it challenging to assess their performance at 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 the hierarchical Bayesian model to obtain their realistic estimates in real world conditions and show how it may be used in situations in which new tests with limited history are presented on the market. Proposed methodology allows for the efficient information pooling, thereby improving on the accuracy of parameter estimates for new tests. The results suggest that the application of hierarchical model on the Czech data led to a considerabile reduction in uncertainty associated with the parameter estimates as well as with potential economic cost implied by false positive test results. The model can thus assist in better informed decision-making and financial planning of both the government and corporations.