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

arXiv: https://arxiv.org/abs/2104.04059 · HTML: https://mlsanity.com/paper/2104.04059/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 186 references that are in the map.
- First posted on arXiv 13 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions](https://mlsanity.com/paper/2006.10532/) (2020)
- [COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification](https://mlsanity.com/paper/2005.10898/) (2020)
- [Using Mobility for Electrical Load Forecasting During the COVID-19 Pandemic](https://mlsanity.com/paper/2006.08826/) (2020)
- [Modeling Dengue Vector Population Using Remotely Sensed Data and Machine Learning](https://mlsanity.com/paper/1805.02590/) (2018)
- [Correlation between Air and Urban Travelling with New Confirmed Cases of COVID-19 A Case Study](https://mlsanity.com/paper/2010.01413/) (2020)
- [Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19](https://mlsanity.com/paper/2010.09648/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs](https://mlsanity.com/paper/2404.16921/) (2024)

## Abstract (arXiv)

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.