Artificial Intelligence Models and Association of Air Pollutants and Novel Coronavirus: A Survey for Asia and Oceania

Abstract

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has led to approximately 704 million confirmed cases of coronavirus disease (COVID-19) and resulted in approximately 7.01 million fatalities worldwide. The present review examines the applications of Artificial Intelligence (AI) models concerning the COVID-19 pandemic and its correlation with air pollutants. The objective of this review is to identify, assess, and synthesize pertinent findings regarding the relationship between air pollution and COVID-19. Initially, a comprehensive set of 549 articles was screened, resulting in the selection of 38 articles from the study region through two stringent rounds of inclusion and exclusion. Given the limited availability of published literature originating from countries in Asia and Oceania, the authors endeavoured to focus specifically on studies from this geographical area. The analysis primarily centred on contextual keywords, methodologies employed, algorithms utilized, and, notably, the specific air pollutants examined, such as particulate matter (PM) including PM2.5 and PM10, as well as associated meteorological parameters. Our findings indicate that a significant portion of the research is concentrated in China, recognized as the initial epicentre of the COVID-19 outbreak. Additionally, most researchers from Asia and Oceania primarily concentrated on PM2.5, followed by studies on meteorological factors and PM10. This review delineates five prospective research avenues for future exploration. Consequently, this article enriches the existing literature by providing researchers with insights into current studies, thereby enhancing the accessibility of available evidence for decision-makers and proposing a potential research agenda for forthcoming investigations.