ArticleIEEE access : practical innovations, open solutions2021
Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled it
- Prenatal Air Pollution Exposure During Late Pregnancy Associates With Food Sensitization at 18 Months.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2026Article
- Inference of latent epidemic regimes and generative simulations reveal how inequality and mobility shape COVID-19 transmission.Scientific reports · 2026Article
- A health and economic evaluation of the spatial spillover effect from measles resurgence.Scientific reports · 2025Article
- Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach.Sustainable cities and society · 2023Article
- Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico.Entropy (Basel, Switzerland) · 2023Article
- Review
- Multi-weight susceptible-infected model for predicting COVID-19 in China.Neurocomputing · 2023Article
- Investigating impacts of COVID-19 on urban mobility and emissions.Cities (London, England) · 2023Article
- Air pollution and mobility patterns in two Ugandan cities during COVID-19 mobility restrictions suggest the validity of air quality data as a measure for human mobility.Environmental science and pollution research international · 2023Article
- COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach.Computer communications · 2023Article
- Statistical Analysis of the Impact of COVID-19 on PMSensors (Basel, Switzerland) · 2022Article
- A Turf-Based Feature Selection Technique for Predicting Factors Affecting Human Health during Pandemic.Life (Basel, Switzerland) · 2022Article
- Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology.Water research · 2022Article
- Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries.International journal of environmental research and public health · 2022Article
- Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study.International journal of environmental research and public health · 2022Article
- Epidemic forecasting based on mobility patterns: an approach and experimental evaluation on COVID-19 Data.Social network analysis and mining · 2022Article
- Machine learning applications for COVID-19 outbreak management.Neural computing & applications · 2022Review
- Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels.International journal of environmental research and public health · 2021Article
- Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.