Primus inter pares: A Comparison and Ranking of COVID-19 Vaccines

2022-01-26T14:14:12+00:00

As the world fights the recent devastating calamity, coronavirus pandemic, humanity experiences the most accelerated vaccine development and vaccination in history. We holistically compare, rate and rank SARS-Cov-2 vaccines as well as vaccine platforms in multi-attributes as the first study in the literature. We use grey relational analysis as a multi-criteria decision-making tool, wider grey systems theory to compare, rate and rank the vaccines. We select 12 leading vaccines and 14 attributes from efficacy rate, safety/reactogenicity and protection against variants to children use, from approvals to prices, to logistics and market share to form a 360-degree comparison. According to equally weighted attributes, Pfizer/BioNTech vaccine is at the top rank. The second rank belongs to Moderna when the third belongs to Sinovac. The top three are followed by Oxford-AstraZeneca, Johnson & Johnson, and others. Pfizer/BioNTech is also at the first rank and followed by Moderna and Novavax with respect to 40% weighted efficacy rate. Our ranking approach, which is unbiased and reproducible employs a Wuhan- originated tool, grey systems theory to rank the vaccines against SARS-CoV-2, another Wuhan- originated agent. Since all vaccines are valuable, our effort is to determine the primus inter pares (= first among equals).

A Classification of Hospitals using Performance Features and Machine Learning Algorithms in the Event of Random Surge in Inpatients and Prediction of Live Discharges

2022-01-26T14:16:51+00:00

In this paper, in phase-1 we classified hospitals of three categories in the presence of seven performance features, using several MATLAB^® machine learning algorithms that comprised ‘Classification learners’, and compared model validation and classification results. We chose the best trained model validated for prediction of future hospital category, while comparing the models based on accuracy rate and AUC (area under ROC curve). Out of the five classifiers that we short listed for final selection, Ensemble Subspace KNN (k-nearest neighbor) emerged as the best classifier, and it predicted the same original hospitals for treatment in the event of a random surge in Inpatients during a pandemic, indicating that those hospitals can handle the surge with the available resources. Then in phase-2 with the new Inpatients and predicted hospitals, we used ‘Regression learners’ and selected the best learner based on lowest RMSE and highest R² to predict Live Discharges. Out of the four regression learners that were chosen for final selection, Linear Support Vector Machines emerged as the best learner, and it did predict Live Discharges after the surge.

Journal of Analytics

Primus inter pares: A Comparison and Ranking of COVID-19 Vaccines

A Classification of Hospitals using Performance Features and Machine Learning Algorithms in the Event of Random Surge in Inpatients and Prediction of Live Discharges