Retrospective Economic Evaluation of Group Testing in the COVID-19 Pandemic

Abstract

Surveillance of diseases in a pandemic is an important part of public health policy. Diagnostic testing at the individual level is often infeasible due to resource constraints. To circumvent these constraints, group testing can be applied. The economic cost evaluation from the payer's perspective typically focuses only on deterministic costs which overlooks the substantial economic impact of productivity losses resulting from quarantine and workplace disruptions. The objective of this article is to develop a mathematical model for a retrospective economic evaluation of group testing that incorporates both deterministic costs and income-based economic loss. Group testing algorithms are revisited and simulated at optimized pool sizes to determine the required number of tests. Income data from the German Socio-Economic Panel are integrated into a mathematical model to capture the economic loss. Afterward, hybrid Monte Carlo experiments are conducted by evaluating the economic cost in the Coronavirus disease 2019 pandemic in Germany. Monte Carlo experiments show that the optimal choice of group testing algorithms changes substantially when income-based economic losses are included. Evaluations considering only deterministic costs systematically underestimate the total economic cost. Algorithms with a longer quarantine duration are less attractive than shorter quarantine duration if income-based economic loss is accounted for. The findings show that current evaluations underestimate the true economic cost. Group testing algorithms with shorter duration and fewer stages are preferred, even when they require a larger number of tests. These results underscore the importance of incorporating income-based economic loss into a mathematical model.

Figures (8)

• Figure 1: Illustrative scheme of the two-stage group testing (GT) algorithm according to dorfman1943. The considered population size is $n = 16$ with pool sizes $s_1 = 4$. Red indicates the infected and blue the non-infected individuals.
• Figure 2: Progress of approximated point prevalence values in the COVID-19 pandemic in German districts Berlin-Mitte, Bremen and Hamburg across the years 2020 to 2024.
• Figure 3: Histogram and kernel density estimates of daily incomes in EUR (capped at 1000) in Berlin-Mitte, Bremen, and Hamburg in the years 2019 to 2020.
• Figure 4: Progress of economic cost in EUR per individual (ECI) for the COVID-19 pandemic for the districts Hamburg, Bremen and Berlin. Solid lines represents the average ECI. Uncertainty is visualized by the range via shaded areas around the average ECI.
• Figure 5: Progress of economic cost in EUR per individual (ECI) for the COVID-19 pandemic in Hamburg for changing population size $n \in \{150,500,1000,5000\}$. Solid lines represents the average ECI. Uncertainty is visualized by the range via shaded areas around the average ECI.