A Study on AI‐based Waste Management Strategies for the COVID‐19 Pandemic. Issue 2 (9th March 2022)

• Record Type: Journal Article
• Authors: Rubab, Saddaf; Khan, Malik M.; Uddin, Fahim; Abbas Bangash, Yawar; Taqvi, Syed Ali Ammar
• Is Part Of: ChemBioEng reviews. Volume 9:Issue 2(2022)
• Page Start: 212
• Page End: 226
• Publication Date: 2022-03-09
• Subjects: Artificial intelligence -- COVID‐19 -- Infectious diseases -- Machine learning -- Solid waste -- Waste management cycle; Chemical engineering -- Periodicals; Biochemical engineering -- Periodicals; Biotechnology -- Periodicals; 660.05
• DOI: 10.1002/cben.202100044
• Languages: English
• ISSNs: 2196-9744

Abstract

Abstract: COVID‐19 has swept across the globe and disrupted all vectors of social life. Every informed measure must be taken to stop its spread, bring down number of new infections and move to normalization of daily life. Contemporary research has not identified waste management as one of the critical transmission vectors for COVID‐19 virus. However, most underdeveloped countries are facing problems in waste management processes due to the general inadequacy and inability of waste management. In that context, smart intervention will be needed to contain possibility of the COVID‐19 spread due to inadequate waste management. This paper presents a comparative study of the artificial intelligence/machine learning based techniques, and potential applications in the COVID‐19 waste management cycle (WMC). A general integrated solid waste management (ISWM) strategy is mapped for both short‐term and long‐term goals of COVID‐19 WMC, making use of the techniques investigated. By aligning current health/waste‐related guidelines from health organizations and governments worldwide and contemporary, relevant research in area, the challenge of COVID‐19 waste management and, subsequently, slowing the pandemic down may be assisted. Abstract : Pandemic like COVID‐19, call for a greater emphasis on the waste management processes and strategies. This can be efficiently managed by the introduction of technology including artificial intelligence (AI) and machine learning (ML), were made to address the gigantic challenge of halting the spread of the viral disease at a global scale as well as on industry work layouts.