Measuring the COVID-19 treatment efficiency in OECD countries: a multiplicative network DEA approach

Abstract

The outbreak and subsequent COVID-19 pandemic became an urgent public hygiene crisis and concern for international society. The significance of evaluating a healthcare system’s performance when dealing with public hygiene hazards generally reflects the preparation and reaction level of a country. Data envelopment analysis (DEA) can assess efficiency by comparing outputs produced by inputs in each decision-making unit (DMU). This research thus employs a multiplicative DEA approach relative to a log-linear technology to construct a network DEA model that measures overall efficiency in a two-stage network structure. The stage efficiencies via a weighted geometric mean aggregate into overall efficiency. We decompose the weighted geometric mean efficiency through means of using the general two-stage structure as a numerical example. Some interesting findings about the change in overall and stage efficiencies appear. First, a variation in the weight of stage efficiency does not change the stage efficiency scores. Second, the stage efficiency scores for the most part remain unchanged under different weights of stage efficiency. Finally, we apply the proposed network DEA model in multiplicative form to evaluate the efficiency of COVID-19 treatment in OECD countries.

Keywords:

• Data envelopment analysis (DEA)
• network DEA
• multiplicative DEA
• COVID-19 treatment efficiency
• healthcare

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This research is supported by the National Natural Science Foundation of China (Nos. 72171122, 72171124, and 72201130) and the National Social Science Foundation of China (23AJY018). This research is also supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX23_2328).