ArticleApplied soft computing2020
Assessing countries' performances against COVID-19 via WSIDEA and machine learning algorithms.
Article in Applied soft computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A multi-phase, multi-method assessment of national COVID-19 vaccination performance with equity analysis.Scientific reports · 2026Article
- Hospital efficiency assessment: a systematic review of DEA and hybrid DEA-machine learning approaches.Frontiers in medical technology · 2026Review
- Article
- A variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach.PeerJ. Computer science · 2024Article
- Research on the location decision-making method of emergency medical facilities based on WSR.Scientific reports · 2023Article
- A network Data Envelopment Analysis to estimate nations' efficiency in the fight against SARS-CoV-2.Expert systems with applications · 2022Article
- Fuzzy-SIRD model: Forecasting COVID-19 death tolls considering governments intervention.Artificial intelligence in medicine · 2022Article
- The applications of MCDM methods in COVID-19 pandemic: A state of the art review.Applied soft computing · 2022Review
- Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study.International journal of environmental research and public health · 2022Article
- An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling.Annals of data science · 2022Article
- COVID-19 Symptoms app analysis to foresee healthcare impacts: Evidence from Northern Ireland.Applied soft computing · 2022Article
- Priority and age specific vaccination algorithm for the pandemic diseases: a comprehensive parametric prediction model.BMC medical informatics and decision making · 2022Article
- A Review on the 40 Years of Existence of Data Envelopment Analysis Models: Historic Development and Current Trends.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Country transition index based on hierarchical clustering to predict next COVID-19 waves.Scientific reports · 2021Article
- Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic.Diagnostics (Basel, Switzerland) · 2021Review
- Supply chain design to tackle coronavirus pandemic crisis by tourism management.Applied soft computing · 2021Article
- Determining the location of isolation hospitals for COVID-19 via Delphi-based MCDM method.International journal of intelligent systems · 2021Article
- An extended fuzzy decision-making framework using hesitant fuzzy sets for the drug selection to treat the mild symptoms of Coronavirus Disease 2019 (COVID-19).Applied soft computing · 2021Article
- Real-time measurement of the uncertain epidemiological appearances of COVID-19 infections.Applied soft computing · 2021Article
- Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network.Applied soft computing · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic, which first spread to the People of Republic of China and then to other countries in a short time, affected the whole world by infecting millions of people and have been increasing its impact day by day. Hundreds of researchers in many countries are in search of a solution to end up this pandemic. This study aims to contribute to the literature by performing detailed analyses via a new three-staged framework constructed based on data envelopment analysis and machine learning algorithms to assess the performances of 142 countries against the COVID-19 outbreak. Particularly, clustering analyses were made using k-means and hierarchic clustering methods. Subsequently, efficiency analysis of countries were performed by a novel model, the weighted stochastic imprecise data envelopment analysis. Finally, parameters were analyzed with decision tree and random forest algorithms. Results have been analyzed in detail, and the classification of countries are determined by providing the most influential parameters. The analysis showed that the optimum number of clusters for 142 countries is three. In addition, while 20 countries out of 142 countries were fully effective, 36% of them were found to be effective at a rate of 90%. Finally, it has been observed that the data such as GDP, smoking rates, and the rate of diabetes patients do not affect the effectiveness level of the countries.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.