Conceptual Framework of a DEA-Driven Decision Support System for Pandemic Response

This paper presents a conceptual framework for assessing the effectiveness of pandemic response measures using Data Envelopment Analysis (DEA) as a decision support tool. The proposed model integrates both discretionary and non-discretionary inputs, as well as desirable and undesirable outputs – extending the classical DEA approach to better reflect the complexity of public health systems in crisis contexts. The paper highlights the methodological advantages of this approach, including its ability to identify efficiency benchmarks and recommend targeted improvements. However, the analysis also underscores a major limitation: the lack of standardized and reliable data across countries significantly undermines the model’s applicability and interpretability. The authors conclude that without international harmonization of health data reporting practices, even the most advanced evaluation models cannot yield credible or actionable insights for pandemic management.