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

2021 ◽  
Author(s):  
Md Mokhlesur Rahman ◽  
Kamal Chandra Paul ◽  
Md. Amjad Hossain ◽  
G. G. Md. Nawaz Ali ◽  
Md. Shahinoor Rahman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Form ◽  
Human Mobility ◽  
Virus Transmission ◽  
Past Research ◽  
Policy Implications ◽  
Viral Spread ◽  
Global Pandemic ◽  
Review Study

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 the 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.

scholarly journals Impact on Air Quality of the COVID-19 Lockdown in the Urban Area of Palermo (Italy)

2020 ◽  
Vol 17 (20) ◽  
pp. 7375 ◽  
Author(s):  
Marcello Vultaggio ◽  
Daniela Varrica ◽  
Maria Grazia Alaimo
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Area ◽  
Human Mobility ◽  
Infected People ◽  
National Crisis ◽  
Global Pandemic ◽  
Pm 10 ◽  
The Impact

At the end of 2019, the first cases of coronavirus disease (COVID-19) were reported in Wuhan, China. Thereafter, the number of infected people increased rapidly, and the outbreak turned into a national crisis, with infected individuals all over the country. The COVID-19 global pandemic produced extreme changes in human behavior that affected air quality. Human mobility and production activities decreased significantly, and many regions recorded significant reductions in air pollution. The goal of our investigation was to evaluate the impact of the COVID-19 lockdown on the concentrations of the main air pollutants in the urban area of Palermo (Italy). In this study, the trends in the average concentrations of CO, NO2, O3, and PM10 in the air from 1 January 2020 to 31 July 2020 were compared with the corresponding average values detected at the same monitoring stations in Palermo during the previous five years (2015–2019). During the lockdown period (10 March–30 April), we observed a decrease in the concentrations of CO, NO2, and particulate matter (PM)10, calculated to be about 51%, 50%, and 45%, respectively. This confirms that air pollution in an urban area is predominantly linked to vehicular traffic.

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

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Md. Mokhlesur Rahman ◽  
Kamal Chandra Paul ◽  
Md. Amjad Hossain ◽  
G. G. Md. Nawaz Ali ◽  
Md. Shahinoor Rahman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Human Mobility

scholarly journals Subtle Changes or Dramatic Perceptions of Air Pollution in Sydney during COVID-19

Environments ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 2
Author(s):  
Peter Brimblecombe ◽  
Yonghang Lai
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
New South ◽  
Human Mobility ◽  
Ground Level ◽  
Physical Reality ◽  
Social Perceptions ◽  
Search Terms ◽  
South Wales ◽  
Significant Difference

The COVID-19 pandemic made it critical to limit the spread of the disease by enforcing human isolation, restricting travel and reducing social activities. Dramatic improvements to air quality, especially NO2, have often characterised places under COVID-19 restrictions. Air pollution measurements in Sydney in April 2019 and during the lockdown period in April 2020 show reduced daily averaged NO2 concentrations: 8.52 ± 1.92 and 7.85 ± 2.92 ppb, though not significantly so (p1~0.15) and PM2.5 8.91 ± 4.94 and 7.95 ± 2.64 µg m−3, again a non-significant difference (p1~0.18). Satellite imagery suggests changes that parallel those at ground level, but the column densities averaged over space and time, in false-colour, are more dramatic. Changed human mobility could be traced in increasing times spent at home, assessed from Google Mobility Reports and mirrored in decreased traffic flow on a major road, suggesting compliance with the restrictions. Electricity demand for the State of New South Wales was low under lockdown in early April 2020, but it recovered rapidly. Analysis of the uses of search terms: bushfires, air quality, haze and air pollution using Google Trends showed strong links between bushfires and pollution-related terms. The smoke from bushfires in late 2019 may well have added to the general impression of improved air quality during lockdown, despite only modest changes in the ground level measurements. This gives hints that successful regulation of air quality requires maintaining a delicate balance between our social perceptions and the physical reality.

scholarly journals Air Pollution’s Impact on the Economic, Social, Medical, and Industrial Injury Environments in China

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 261
Author(s):  
Zhong Fang ◽  
Pei-Ying Wu ◽  
Yi-Nuo Lin ◽  
Tzu-Han Chang ◽  
Yung-ho Chiu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Production Efficiency ◽  
Past Research ◽  
Efficiency Score ◽  
High Income ◽  
Global Public Health ◽  
Economic Activities ◽  
Middle Income ◽  
Safety And Health

In this era of rapid economic development, it is inevitable that economic activities eventually cause serious damage to the environment’s air quality, making it the focus of global public health. If the treatment efficiency of medical accidents can be improved, then this can significantly stabilize society and improve production efficiency. Past research has mainly focused on work safety and health issues, seldom discussing economic, social, medical, and environmental pollution issues together, and, most generally, adopted static methods that fail to recognize how air pollution affects the overall economy, society, medical care, and external environment. In order to more deeply understand the changes among social, economic activities, and environmental issues due to air pollution, this study proposes a meta-two-stage undesirable dynamic DDF (Direction Distance Function) that, under an exogenous model, divides the 30 provinces of China into high-income regions and middle-income regions and explores the economic, social, medical, and environmental efficiencies between the two areas to resolve the lack of related static analyses. The empirical results are as follows. (1) The AQI (air quality index) significantly impacts the efficiency of medical injuries in various regions. (2) When the AQI is considered, the medical insurance expenditure efficiency score value of high-income areas is lower than the value without the AQI. (3) When the AQI is considered, the efficiency value of the number of work injury insurance benefits in the middle-income area is lower than the efficiency value without the AQI.

scholarly journals Riding the waves: the ongoing impact of COVID-19 on a national surgical training cohort

2021 ◽  
Author(s):  
Orla Hennessy ◽  
Amy Lee Fowler ◽  
Conor Hennessy ◽  
David Brinkman ◽  
Aisling Hogan ◽  
...  
Keyword(s):  
Surgical Training ◽  
World Health Organisation ◽  
World Health ◽  
Training Methods ◽  
Viral Spread ◽  
Surgical Services ◽  
Global Pandemic ◽  
Surgical Trainees ◽  
Secondary Survey ◽  
The Impact

Abstract Background The World Health Organisation declared a global pandemic on the 11 March 2020 resulting in implementation of methods to contain viral spread, including curtailment of all elective and non-emergent interventions. Many institutions have experienced changes in rostering practices and redeployment of trainees to non-surgical services. Examinations, study days, courses, and conferences have been cancelled. These changes have the potential to significantly impact the education and training of surgical trainees. Aim To investigate the impact of the COVID-19 pandemic on training, educational, and operative experiences of Irish surgical trainees. Methods Surgical trainees were surveyed anonymously regarding changes in working and educational practices since the declaration of the COVID-19 pandemic on 11 March 2020. The survey was circulated in May 2020 to both core and higher RCSI surgical trainees, when restrictions were at level five. Questions included previous and current access to operative sessions as well as operative cases, previous and current educational activities, access to senior-led training, and access to simulation-/practical-based training methods. A repeat survey was carried out in October 2020 when restrictions were at level two. Results Overall, primary and secondary survey response rates were 29% (n = 98/340) and 19.1% (n = 65/340), respectively. At the time of circulation of the second survey, the number of operative sessions attended and cases performed had significantly improved to numbers experienced pre-pandemic (p < 0.0001). Exposure to formal teaching and education sessions returned to pre-COVID levels (p < 0.0001). Initially, 23% of trainees had an examination cancelled; 53% of these trainees have subsequently sat these examinations. Of note 27.7% had courses cancelled, and 97% of these had not been rescheduled. Conclusion Surgical training and education have been significantly impacted in light of COVID-19. This is likely to continue to fluctuate in line with subsequent waves. Significant efforts have to be made to enable trainees to meet educational and operative targets.

scholarly journals Machine Learning-Based Prediction of Air Quality

2020 ◽  
Vol 10 (24) ◽  
pp. 9151
Author(s):  
Yun-Chia Liang ◽  
Yona Maimury ◽  
Angela Hsiang-Ling Chen ◽  
Josue Rodolfo Cuevas Juarez
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Prediction Models ◽  
Superior Performance ◽  
Support Vector ◽  
Economic Activities ◽  
Adaptive Boosting ◽  
Series Of Experiments ◽  
Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

scholarly journals Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021

2021 ◽  
Author(s):  
Shuhei Nomura ◽  
Yuta Tanoue ◽  
Daisuke Yoneoka ◽  
Stuart Gilmour ◽  
Takayuki Kawashima ◽  
...  
Keyword(s):  
Care Delivery ◽  
Human Mobility ◽  
Age Groups ◽  
Virus Transmission ◽  
Initial Response ◽  
Health Care Delivery System ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The Status

AbstractIn the COVID-19 era, movement restrictions are crucial to slow virus transmission and have been implemented in most parts of the world, including Japan. To find new insights on human mobility and movement restrictions encouraged (but not forced) by the emergency declaration in Japan, we analyzed mobility data at 35 major stations and downtown areas in Japan—each defined as an area overlaid by several 125-meter grids—from September 1, 2019 to March 19, 2021. Data on the total number of unique individuals per hour passing through each area were obtained from Yahoo Japan Corporation (i.e., more than 13,500 data points for each area). We examined the temporal trend in the ratio of the rolling seven-day daily average of the total population to a baseline on January 16, 2020, by ten-year age groups in five time frames. We demonstrated that the degree and trend of mobility decline after the declaration of a state of emergency varies across age groups and even at the subregional level. We demonstrated that monitoring dynamic geographic and temporal mobility information stratified by detailed population characteristics can help guide not only exit strategies from an ongoing emergency declaration, but also initial response strategies before the next possible resurgence. Combining such detailed data with data on vaccination coverage and COVID-19 incidence (including the status of the health care delivery system) can help governments and local authorities develop community-specific mobility restriction policies. This could include strengthening incentives to stay home and raising awareness of cognitive errors that weaken people's resolve to refrain from nonessential movement.

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