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.

翻译：大流行期间疾病监测是公共卫生政策的重要组成部分。受资源限制，个体层面的诊断检测往往难以实施。为克服这些限制，可采用群体检测方法。从支付方角度进行的经济成本评估通常仅关注确定性成本，这忽视了隔离及工作场所中断导致的生产力损失所蕴含的巨大经济影响。本文旨在建立一种数学模型，用于对纳入确定性成本与基于收入的经济损失的群体检测进行回顾性经济评估。重新审视群体检测算法，并在优化池规模下进行模拟，以确定所需检测次数。将德国社会经济面板数据中的收入数据整合至数学模型中以量化经济损失。随后，通过评估德国2019冠状病毒病大流行期间的经济成本开展混合蒙特卡洛实验。蒙特卡洛实验表明，当纳入基于收入的经济损失时，群体检测算法的最优选择会发生显著变化。仅考虑确定性成本的评估系统性低估了总经济成本。若考虑基于收入的经济损失，隔离时间较长的算法较隔离时间较短的算法吸引力更弱。研究结果表明，当前评估低估了真实经济成本。即使需要更多检测次数，更短隔离时间且阶段更少的群体检测算法也更受青睐。这些结果凸显了将基于收入的经济损失纳入数学模型的重要性。