2015; Tahir Ul Qamar et al. 2020; Cleemput et al. 2020; Elfiky 2020; Ahmed et al.

2020; Wang 2020; Kim et al. 2020; Yue et al. 2020).

It is critical to managing an epidemic of infectious diseases that threaten public

health. Currently, the COVID-19 pandemic has emerged as a significant threat to the

world, and its management is of immense importance for every nation to save

humanity. In this respect, information technology will play a critical role, as its

use in COVID-19 emergency management in terms of prevention/mitigation, pre-

paredness, response, and recovery is vital. A variety of IT-based systems may be

helpful in the direction of outbreaks, especially during the response phase. Surveil-

lance technologies, artificial intelligence, computational methods, remote sensing

sensors, Internet services, and geographic information systems (GIS) are among

them (Asadzadeh et al. 2020). Many other viral diseases, such as H1N1, SARS, and

MERS, have benefited from information technology (Cai et al. 2005; Xie et al. 2005;

De Groot et al. 2013; Bogoch et al. 2016; Lan et al. 2016; Sandhu et al. 2016; Francis

et al. 2017; Rovetta and Bhagavathula 2020; Song et al. 2020).

1.3

Role of Artificial Intelligence (AI) in Epidemiology

The propagation of infection can be detected using artificial intelligence. In the case

of the ongoing COVID-19, a health monitoring AI platform, “BlueDot,” located in

Toronto, used big data analytics to map and forecast the virus’s transmission from

Wuhan to Tokyo after its first arrival (The Medical Futurist 2020). The use of deep

learning algorithms, which assist in resolving complex problems and improving the

reliability of performance, is the concept on which AI operates. Consequently, AI

assists in the accelerated detection of positive cases and the control and prevention of

COVID-19 outbreaks (Yu et al. 2020; Hu et al. 2020; Xu et al. 2020; Xie et al. 2020;

Srinivasa Rao and Vazquez 2020; McCall 2020; Vaishya et al. 2020; Ghoshal and

Tucker 2020; Zhang et al. 2020; Bherwani et al. 2020).

Because of its numerous strengths, AI has been seen to be effective in protecting

healthcare personnel by supplying them with reliable knowledge and guidance

(McCall 2020). Deep learning has been used in several studies, including lung

infection quantification, tracing, improving diagnosis, patient management, fast

screening, and drug discovery (Asadzadeh et al. 2020; Shan et al. 2020).

1.4

Drug Discovery-Associated Technologies

In “omics” techniques, such as genomics, epigenomics, transcriptomics, proteomics,

and metabolomics, significant advances have been made. These are also known as

system-based methods, and they can profile and monitor molecular markers, such as

biomarkers (BMs), for a variety of diseases by combining clinical, physiological,

and pathobiological anomalies. This helped clinicians and scientists develop a

learning data set that allowed them to obtain a deeper understanding of disease

pathogenesis at the molecular level.

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