The Application of Machine Learning in the Corona Era, With an Emphasis on Economic Concepts and Sustainable Development Goals

Table 1

Previousresearches about the prediction of AI/ML and COVID-19


Author(s) (year)

Journal Name




Zawbaa et al., (2021)


International Journal of Clinical Practice

Prediction and forecasting different countries daily confirmed-cases and daily death-cases

The results proved usefulness in modelling and forecasting the end status of the virus spreading based on specific regional and health support variables.


Gray et al., (2021)


BMJ Health & Care Informatics

Training machine learning models to predict Covid-19 cases growth and understanding the social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties

African American and Asian racial demographics present comparable, and contrasting, patterns of risk depending on locality


Rios et al., (2021)


Scientific reports

Presented a temporal analysis on the number of new cases and deaths among countries using artificial intelligence

1. They showed the historical infection path taken by specific countries and emphasize changing points that occur when countries move between clusters with small, medium, or large number of cases. 2. They estimated new waves for specific countries using the transition index.


Malki et al., (2021)


Environmental science and pollution research

Applying machine learning approaches to predict the spread of Covid-19 in many countries.

Covid-19 infections will greatly decline during the first week of September 2021 when it will be going to an end shortly afterward.


Muhammad et al., (2021)


SN computer science

Prediction of Covid-19 infection (positive and negative cases in Mexico) using ML algorithms such as logistic regression, decision tree, SVM, naïve Bayes and ANN.

Decision tree model has the highest accuracy of 94.99% while the support vector machine model has the highest sensitivity of 93.34% and Naïve Bayes model has the highest specificity of 94.30%.


Kuo et al., (2022)


International Journal of Medical Informatics

The accuracy of machine learning approaches using non-image data for the prediction of Covid-19: A meta-analysis

The results show that non-image data can be used to predict Covid-19 with an acceptable performance. Further, class imbalance and feature selection are suggested to be incorporated whenever building models for the prediction of Covid-19, thus improving further diagnostic performance.


Mohan et al., (2022)


Computers in Biology and Medicine

Predicting the impact of the third wave of Covid-19 in India using hybrid statistical machine learning models: A time series forecasting and sentiment analysis approach

A spike in daily confirmed and cumulative confirmed cases was predicted in India in the next 180 days based on the past time series data. The results were validated using various analytical tools and evaluation metrics, producing a root mean square error (RMSE) of 0.14 and a mean absolute percentage error (MAPE) of 0.06. The Natural Language Processing (NLP) processing results revealed negative sentiments in most articles and blogs, with few exceptions.