Air Pollution Detection in Covid-19 Ward: An Artificial Intelligence Approach

The world has faced a pandemic situation due to COVID-19. The dearth of understanding of germs, the scope of the phenomena, and the rapidity of contamination highlight many points in the new techniques for studying these events. Artificial intelligence approaches could be helpful in assessing data from virus-affected locations. The goal of this research is to look into any links between air quality and pandemic propagation. We also assess how well machine learning algorithms perform when it comes to anticipating new cases. We present a cross-correlation analysis of everyday COVID-19 instances and ecological parameters such as heat, humidification, and contaminants in the atmosphere. Our research reveals a strong link between several environmental factors and the propagation of germs. An intelligent trained model using ecological characteristics may be able to forecast the number of infected cases accurately. This technique may be beneficial in assisting organizations in taking appropriate action about inhabitants’ protection and prevalent response. Temperature and ozone are adversely connected with confirmed cases whereas air particulate matter and nitrogen dioxide are positively correlated. We created and tested three separate predictive models to see if these technologies can be used to forecast the pandemic's progression.

Keywords: Air pollution detection, AI, COVID-19 pandemic, Related analysis.