# Applications of Machine Learning and High-Performance Computing in the Era of COVID-19
## 1 Introduction  
The novel coronavirus disease 2019 (COVID-19) has drastically changed the era of information gathering, processing, analytics, use, distribution, and removal. Due to the COVID-19 pandemic, a huge amount of data is collected by various organizations to estimate the probability of exposure or tracing infected individuals [1]. In addition, this pandemic has increased the reliance on digital technologies to analyze people's mobility and monitor people's compliance with the quarantine guidelines [2]. In addition, many companies have helped governments with innovative technologies to control the COVID-19 crisis [3]. The role of advanced technologies has been dominant in effectively handling this pandemic. Many countries have better control of the pandemic by utilizing technology and human resources simultaneously [4]. The innovative features of each technology have helped to curb the disease spread. The unique features of this pandemic such as estimating the possibility of infection, the identification of potentially contaminated places, contact tracing, mobility analysis, flow modeling, infected people's information sharing, data analytics, high-risk-zone geofencing, compliance monitoring, trends prediction, estimating the likelihood of this pandemic's end, heterogeneous source data fusion, and the adoption of remote technologies have increased the utilization of digital technology. We present the taxonomy of the epidemic features that demand the utilization of advanced techniques such as machine learning (ML) and high-performance computing (HPC) in Figure 1. Thesefeatures are unique to the COVID-19 era and require the utilization of advanced technologies to curb the spread. Through the utilization of ML and HPC, significant mitigation and control of this pandemic can be achieved. Despite the enormous benefits of technologies in the COVID-19 era, the adoption of digital solutions has been low, mainly due to privacy issues [5]. Furthermore, some countries have adopted digital solutions by underestimating privacy requirements, and significant results have been obtained in terms of disease control. Moreover, people's anxiety and worries remain higher in such countries. For instance, in South Korea, credit card data, CCTV data, mobile signals, and random calls were adopted to trace the contacts of infected people. Similarly, China used advanced decision support systems to control the disease. In some countries, mobile apps were employed to find potently suspected patients. Through a detailed synthesis of the literature and analysis of multiple available apps and reports, we provide the statistics of ten countries in terms of higher adoption of AI over a period of almost one and half years in Figure 2. Furthermore, we provide insights into the investment in technology by companies around the world to combat the crisis caused by the ongoing pandemic [6]. Figure 1: Taxonomy of the epidemic features that demand the utilization of advanced techniques such as ML and HPC. Figure 2: Statistics about (**a**) investment in digital solutions and (**b**) the adoption of AI around the world. To date, many studies have provided coverage of AI applications in the era of COVID-19. However, these studies have mainly focused on the general applications of the technology in the education sector, industry areas, tourism sector, etc. However, a concrete overview of ML and HPC usage in the COVID-19 context, and their usefulness in the mitigation and control of this pandemic, has not been discussed in previous studies. To address these issues, this study covers the applications of ML and HPC considering epidemic features (i.e., epidemic control measures, data life cycle in epidemic systems, and general application of ML and HPC). More specifically, we highlight the role, need, and utility of ML and HPC technologies in the COVID-19 era. To this end, a compact overview of the fidelity of ML and HPC techniques in the era of COVID-19 is demonstrated with practical examples.