Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review

TL;DR

This review analyzes how machine learning techniques have been used to study the interactions between COVID-19, human mobility, and air quality, highlighting the impacts of lockdowns and urban factors on pandemic dynamics and environmental health.

Contribution

It provides a comprehensive synthesis of existing research on ML applications in understanding COVID-19 spread, mobility patterns, and air quality changes during the pandemic.

Findings

Lockdowns improved air quality by reducing pollutants.

Urban form and social factors significantly influence virus transmission.

People shifted to private transportation to reduce health risks.

Abstract

The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. during the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.