Abstract
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).
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).
| Original language | English |
|---|---|
| Pages (from-to) | 1363-1367 |
| Number of pages | 5 |
| Journal | Information Systems Frontiers |
| Volume | 23 |
| Issue number | 6 |
| Early online date | 3 Nov 2021 |
| DOIs | |
| Publication status | Published - 1 Dec 2021 |