Artificial Intelligence-based Disease Surveillance Amid COVID-19 and Beyond: A Systematic Review Protocol

Abstract

Background: Infectious diseases are dangerous and deadly. As the leading causes of morbidity and mortality in all demographics across the world, infectious diseases carry substantial social, economic, and healthcare costs. Unlike previous global health crises, health experts now have access to more advanced tools and techniques to understand pandemics like COVID-19 better and faster; one such class of tools is artificial intelligence (AI) enabled disease surveillance systems. AI-based surveillance systems allow health experts to perform rapid mass infection prediction to identify potential disease cases, which is integral to understanding transmission and curbing the spread of the pandemic. However, while the importance of AI-based disease surveillance mechanisms in pandemic control is clear, what is less known is the state-of-the-art application of these mechanisms in countries across the world. Therefore, to bridge this gap, we aim to systematically review the literature to identify (1) how AI-based disease surveillance systems have been applied in counties worldwide amid COVID-19, (2) the characteristics and effects of these applications regarding the control of the spread of COVID-19, and (3) what additional disease surveillance resources such as database, AI-based tools and techniques that can be further added to the current toolbox in the fight against COVID-19. Methods: To locate research on AI-based disease surveillance amid COVID-19, we will search databases including PubMed, IEEE Explore, ACM Digital Library, and Science Direct to identify all potential records. Titles, abstracts, and full-text articles were screened against eligibility criteria developed a priori. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures was adopted as the reporting framework.Results: NA for now Conclusions: Findings of our study will fill an important void in the literature, as no research has systematically reviewed available AI-based disease surveillance in the context of COVID-19. As the world continues to reel from COVID-19, it is important to identify cost-effective AI-based disease surveillance mechanisms that can detect COVID-19 cases and explain how one COVID-19 case turns into many cases, so that better prevention measures can be established to curb the spread of the COVID pandemic in a timely manner. Study Protocol Registration: PROSPERO CRD42020204992

Artificial Intelligence: As an innovative technique in healthcare sector and as a potent technique to fight against COVID-19

“…[76] The crosssectional view of SARS-CoV-2, which is comprised of spike protein (S), nucleocapsid protein (N), hemagglutinin-esterase dimer (HE), membrane glycoprotein/ matrix (M), an envelope protein (E), and single-strand RNA, non-segmented, enveloped. [77] The first case of corona virus infected patient was recorded in December 2019 in Wuhan city, Hubei, China and afterward, the infected cases increased so rapidly throughout the world that the World Health Organization (WHO) has declared it a Public Health Emergency of International Concern (PHEIC) on January 30, 2020. [78]…”

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

“…The cross-sectional view of SARS-CoV-2 shown in Fig. 1 , which is comprised of spike protein (S), nucleocapsid protein (N), hemagglutinin-esterase dimer (HE), membrane glycoprotein/ matrix (M), an envelope protein (E), and single-strand RNA, non-segmented, enveloped ( Su et al, 2020 ). The first case of coronavirus infected patient was recorded in December 2019 in Wuhan city, Hubei, China and afterward, the infected cases increased so rapidly throughout the world that the World Health Organization (WHO) has declared it a Public Health Emergency of International Concern (PHEIC) on January 30, 2020 ( Deng et al, 2020 , Sohrabi et al, 2020 ).…”