| Literature DB >> 35958804 |
Miguel Alves Pereira1,2, Duarte Caldeira Dinis2, Diogo Cunha Ferreira3, José Rui Figueira2, Rui Cunha Marques3.
Abstract
The ongoing outbreak of SARS-CoV-2 has been deeply impacting health systems worldwide. In this context, it is pivotal to measure the efficiency of different nations' response to the pandemic, whose insights can be used by governments and health authorities worldwide to improve their national COVID-19 strategies. Hence, we propose a network Data Envelopment Analysis (DEA) to estimate the efficiencies of fifty-five countries in the current crisis, including the thirty-seven Organisation for Economic Co-operation and Development (OECD) member countries, six OECD prospective members, four OECD key partners, and eight other countries. The network DEA model is designed as a general series structure with five single-division stages - population, contagion, triage, hospitalisation, and intensive care unit admission -, and considers an output maximisation orientation, denoting a social perspective, and an input minimisation orientation, denoting a financial perspective. It includes inputs related to health costs, desirable and undesirable intermediate products related to the use of personal protective equipment and infected population, respectively, and desirable and undesirable outputs regarding COVID-19 recoveries and deaths, respectively. To the best of the authors' knowledge, this is the first study proposing a cross-country efficiency measurement using a network DEA within the context of the COVID-19 crisis. The study concludes that Estonia, Iceland, Latvia, Luxembourg, the Netherlands, and New Zealand are the countries exhibiting higher mean system efficiencies. Their national COVID-19 strategies should be studied, adapted, and used by countries exhibiting worse performances. In addition, the observation of countries with large populations presenting worse mean efficiency scores is statistically significant.Entities:
Keywords: Data Envelopment Analysis; Efficiency measurement; SARS-CoV-2; Series structure; Simulation
Year: 2022 PMID: 35958804 PMCID: PMC9355747 DOI: 10.1016/j.eswa.2022.118362
Source DB: PubMed Journal: Expert Syst Appl ISSN: 0957-4174 Impact factor: 8.665