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. 2021 May 11:9:72420-72450.
doi: 10.1109/ACCESS.2021.3079121. eCollection 2021.

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

Md Mokhlesur Rahman  1   2 , Kamal Chandra Paul  3 , Md Amjad Hossain  4 , G G Md Nawaz Ali  5 , Md Shahinoor Rahman  6 , Jean-Claude Thill  7
Affiliations

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

Md Mokhlesur Rahman et al. IEEE Access. .

Abstract

The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the 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 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel 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. 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 explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

Keywords: COVID-19; air quality; coronavirus; human mobility; machine learning; pandemic; public health; review; spatio-temporal analysis.

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Figures

FIGURE 1.
FIGURE 1.
COVID-19 confirmed cases and deaths (%) by WHO region (January 6, 2021).
FIGURE 2.
FIGURE 2.
Longitudinal changes in mobility in selected countries.
FIGURE 3.
FIGURE 3.
Conceptual framework of the study. Topics broached in the different sections are color coded according to the labeling used in the legend.
FIGURE 4.
FIGURE 4.
Key reasons to choose transportation modes before and during the COVID-19 pandemic, modified from.
FIGURE 5.
FIGURE 5.
Impact of mobility on COVID-19 infection.
FIGURE 6.
FIGURE 6.
Impacts of social distancing on mobility and COVID-19 pandemic.
FIGURE 7.
FIGURE 7.
Daily mean air quality change in 2020 compared to 2019 in the US and Europe. Data source: EPA and World Air Quality Index Project, available at .
FIGURE 8.
FIGURE 8.
Longitudinal changes in formula image emissions in the world under confinement scenarios.
FIGURE 9.
FIGURE 9.
Changes in formula image levels in 2020 compared to 2019 in 10 major global cities during COVID-19 related lockdown periods between February and April 2020 .
See this image and copyright information in PMC

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