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

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 nonpharmaceutical 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 respiratoryrelated 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 spatiotemporal 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.

Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics

“…Considering the enormous computational capabilities of machine learning techniques to handle complex and multifaceted problems, future studies should apply machine learning algorithms to classify and label tweets and other social media posts with improved accuracy to, thus, better reflect real-world contexts [17,73,74]. This study employed the PSS framework and provides a strong basis for formalizing public sentiment driven influence on policy formulation and implementation.…”

“…The success experienced by countries across the world has depended on the effectiveness of their COVID-19 public policies pertaining to healthcare, communication, education, motivation and non-pharmaceutical interventions (NPIs), such as social distancing. Given that the COVID-19 vaccine was not available in the early stages of the outbreak, public policies initially focused on various NPIs (e.g., lockdown, restrictions on mass gathering, bans on travel, border closing, testing, and contact tracing), and economic stimuli (e.g., donations, loans, and debt relief) were implemented to contain the pandemic and mitigate the associated risks [14][15][16][17][18].…”

Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network

“…The extant literature shows that, from the inception of the pandemic, researchers from around the world have been developing and implementing COVID-19 prediction models to understand the severity of the pandemic, delineate associated factors of virus infection, recovery, and death, and support the design of effective policies and operational measures to manage this unprecedented public health crisis 1 , 15 , 20 – 22 . A wide range of methods and tools has been used in predictive models.…”

Enabling and Emerging Sensing Technologies for Crowd Avoidance in Public Transportation: A Review

“…Big data collected thanks to the pervasive use of ICT/IoT can help, indeed, to discover the relationships between human mobility and resource use, thus entailing great opportunities for smart city development. In [19], instead, the Authors pursue the objective to identify data sources and ML approaches to properly estimate the impact of COVID-19 on human mobility reduction. In particular, [19] investigates the consequences of the pandemic on mobility patterns of urban populations, by quantifying even the impact of mobility reduction on improving air quality in urban areas.…”

“…In [19], instead, the Authors pursue the objective to identify data sources and ML approaches to properly estimate the impact of COVID-19 on human mobility reduction. In particular, [19] investigates the consequences of the pandemic on mobility patterns of urban populations, by quantifying even the impact of mobility reduction on improving air quality in urban areas.…”