1 Introduction

COVID-19 has become the most significant challenge human beings have encountered since World War 2 (WW2). In the USA, more COVID related deaths have been reported than the combined fatalities of the Pearl Harbor War and the September 11 terror attacks. COVID-19 itself is highly infectious and the speed at which it can mutate is rapid, leading to multiple varieties and strands of active coronaviruses, and rapid increases in the numbers of infected cases and deaths. The virus has infected more than 217 million of the population worldwide before the end of August 2021, yet in early March 2020, the total infected cases were still not reaching 100,000 (WHO, 2021).

The global challenge has changed how we live, with social distancing measures and face masks mandates, and has shone a spotlight on our healthcare capabilities, highlighting shortages of medical resources and capacity, and raising issues of equitable access to vaccines and drugs. Governments of different countries have been challenged to deal with the crisis using limited resources and have emphasized different policies suiting their national strategies (Ecke, 2020; Ranney et al., 2020).

Machine Learning, AI and Big Data research, scientists can offer recommendations, new discoveries and predictive methods in addressing both societal impact (Gupta et al., 2018) and medical services (van der Sommen et al., 2020). Big Data and cloud computing (Chang, 2014; Hosseinian-Far et al., 2018), the Internet of Things (Sicari et al., 2016), and Artificial Intelligence techniques (Vaishya et al., 2020) have significantly contributed to developing an understanding of the virus, identifying and developing treatments and managing and tracking the social and economic impact of the pandemic, and predicting trends (Tuli et al., 2020). Scientists can better understand the structure and weaknesses of virus variants through simulations offered by ML algorithms, leading to vaccine development with higher efficacy (Rotondo et al., 2021). Challenges addressed range from COVID-19 diagnosis and infection confirmation based on a real-time reverse-transcriptase polymerase chain reaction (Kim et al., 2020) to privacy protection and secure transmission of messages among medical professionals (Wang et al., 2020).

2 Selection of Papers

We have sought unpublished and high-quality work based on unique Machine Learning, AI and Big Data methods and findings. Best paper winners and top authors from IIoTBDSC 2020 have also been invited. We applied rigorous review processes, selecting six papers out of twenty-five submissions, which we briefly summarise.

The use of AI for patient screening and disease detection is addressed by two articles. Elakkiya et al. (2021) demonstrate ‘COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking’, which proposes bifold COVID_SCREENET, a novel software architecture to extract features of scanned images and use Deep Learning models that work under multi-class classification. Convolutional Neural Networks (CNNs) are used to analyze images and identify features for faster diagnostics, and the authors claim 100% accuracy in performing multi-class classification for 2216 chest X-ray images collected between April and May 2020.

Singh et al. (2021) develop ‘A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients’, which describes a method for detecting COVID-19 using a synthetic dataset with COVID-19 aimed at determining if a patient has the disease. 15 COVID-19 symptoms feed the classifier and the result is a decision about the patient’s illness. A comparison to other classifiers shows improved accuracy on prior approaches. The approach is potentially applicable to other pandemic diseases and particularly promising for use in telemedicine, where direct access to patients is not possible.

Patient privacy cannot be sacrificed in the rush to analyze data. Zhang et al. (2021) address privacy issues arising from the training of Generative Adversarial Networks (GAN) to recognize COVID-19 pneumonia. Instead Federated Learning, which preserves the privacy of patients, is proposed as an alternative. Comparison of the algorithm’s performance against other systems is encouraging for its use in contexts where patient privacy is important. The approach is already used across several hospitals for collective training of COVID-19 models without sharing the original data.

Two papers address the well-being of the population under the pressure of the pandemic. Both use Twitter as their cohort but use different classification techniques. Kaur et al. (2021) present ‘A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets’. They analyze the mental state of twitter users by applying a Hybrid Heterogeneous Support Vector Machine (H-SVM) algorithm for accurate, rapid and large-scale Twitter sentiment analysis, classifying Twitter hashtags as positive, negative and neutral. The proposed algorithm measured by precision, recall, f-measure and accuracy metrics outperform Recurrent Neural Networks and Support Vector Machines.

Choudrie et al. (2021) analyze the financial and emotional state of Twitter users. An advanced deep learning technique of Transfer Learning combined with a Robustly Optimized BERT Pretraining Approach (RoBERTa) is used to analyze 2 million tweets collected during February–June 2020. A multi-class emotion classifier system was formed using RoBERTa and the collated Twitter dataset. The performance analysis shows that the proposed work gives the classification accuracy of 80.33% and an average MCC score of 0.78, which is better than existing AI-based emotion classification methods.

Finally, Piccialli et al. (2021) present ‘The Role of Artificial Intelligence in Fighting the COVID-19 pandemic’. The authors provide a wider data science perspective to the application of AI, with a general overview of the dynamics of the pandemic and a critical evaluation of the papers published and datasets used on the COVID-19 outbreak. The detailed AI methods available to develop effective COVID-19 analysis provide a useful insight for researchers, particularly those working in the health domain. The paper discusses the stages of data flows from GP surgery, Hospitals, and research centers and the potential applications of AI using this data: outbreak prediction, spread tracking, diagnosis, drug production and drug repurposing.

3 Open Research Direction

There are many open research directions on this topic, and here we highlight three.

3.1 More and Better COVID-19 Diagnosis

With the advancement of pioneering AI and Data Science techniques, more and better diagnoses will be available. These may include innovative methods to improve the accuracy of medical analyses, such as PCR diagnosis, or totally new methods, such as alternative solutions similar to COVID_SCREENET and FedDPGAN.

3.2 A Better Prediction of Health and State of Well-Being

As the general public is fully aware of their health and the state of well-being, AI and Data Science can make better predictions in real-time. This can be used to collect the users’ data through smart devices, and intelligent algorithms can compute different predicted outcomes based on users’ needs. Medical institutes and hospitals can also develop their own services to monitor the health and state of well-being of patients contracting with COVID-19 and other diseases.

3.3 Sentiment Analysis of Citizens of Particular Countries and the World in General

Measurement of sentiment analysis has become increasingly important because it provides valuable insights to understand how people feel towards new policies and new rules related to the rapid updates in COVID-19 transmission. The general public’s views on key agendas, such as economic recovery plans, employment, education, food supply and rules for social events, can also be analyzed through sentiment analysis.

4 Conclusion

We are delighted to present these six selected papers that demonstrate the use of intelligent AI methods to conduct COVID-19 and related analyses, and AI and Data Science methods will continue to enhance the abilities to investigate impacts, analyses and current interesting research topics related to the immediate pandemic and beyond.

Finally, we are grateful to the Editors-in-Chief of Information Systems Frontiers for giving us the opportunity to lead and organize this special issue. We look forward to future opportunities to serve the research community.