Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

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Maintaining proper health records improves machine learning predictions for novel 2019-nCoV

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01537-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Koffka Khan ◽

Emilie Ramsahai

Keyword(s):

Machine Learning ◽

Reporting Rate ◽

Machine Learning Techniques ◽

Patient Treatment ◽

Third Wave ◽

The United Kingdom ◽

Learning Techniques ◽

And Gender ◽

Novel Coronavirus ◽

The Impact

Abstract Background An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major countries such as China, USA, Italy, France and the United Kingdom. We present outcome (‘recovered’, ‘isolated’ or ‘death’) risk estimates of 2019-nCoV over ‘early’ datasets. A major consideration is the likelihood of death for patients with 2019-nCoV. Method Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we used machine learning techniques (AdaBoost, bagging, extra-trees, decision trees and k-nearest neighbour classifiers) on two 2019-nCoV datasets obtained from Kaggle on March 30, 2020. We used ‘country’, ‘age’ and ‘gender’ as features to predict outcome for both datasets. We included the patient’s ‘disease’ history (only present in the second dataset) to predict the outcome for the second dataset. Results The use of a patient’s ‘disease’ history improves the prediction of ‘death’ by more than sevenfold. The models ignoring a patent’s ‘disease’ history performed poorly in test predictions. Conclusion Our findings indicate the potential of using a patient’s ‘disease’ history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This development can have a positive effect on predictive patient treatment and can result in easing currently overburdened healthcare systems worldwide, especially with the increasing prevalence of second and third wave re-infections in some countries.

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Maintaining proper health records improves machine learning predictions for novel 2019-nCoV

10.21203/rs.3.rs-33551/v2 ◽

2020 ◽

Author(s):

KOFFKA KHAN ◽

Emilie Ramsahai

Keyword(s):

Machine Learning ◽

Reporting Rate ◽

Machine Learning Techniques ◽

Patient Treatment ◽

Third Wave ◽

The United Kingdom ◽

Learning Techniques ◽

And Gender ◽

Novel Coronavirus ◽

The Impact

Abstract Background: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to aect the whole world including major countries such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of 2019-nCoV over 'early' datasets. A major consideration is the likelihood of death for patients with 2019-nCoV.Method: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we used machine learning techniques (AdaBoost, bagging, extra-trees, decision trees and k-nearest neighbour classiers) on two 2019-nCoVdatasets obtained from Kaggle on March 30, 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. We included the patient's 'disease' history (only present in the second dataset) to predict the outcome for the second dataset.Results: The use of a patient's 'disease' history improves the prediction of 'death' by more than 7-fold. The models ignoring a patent's 'disease' history performed poorly in test predictions.Conclusion: Our ndings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This development can have a positive eect on predictive patient treatment and can result in easing currently overburdened healthcare systems worldwide, especially with the increasing prevalence of second and third wave re-infections in some countries.

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Maintaining proper health records improves machine learning predictions for novel 2019-nCoV

10.21203/rs.3.rs-33551/v3 ◽

2021 ◽

Author(s):

KOFFKA KHAN ◽

Emilie Ramsahai

Keyword(s):

Machine Learning ◽

Reporting Rate ◽

Machine Learning Techniques ◽

Patient Treatment ◽

Third Wave ◽

The United Kingdom ◽

Learning Techniques ◽

And Gender ◽

Novel Coronavirus ◽

The Impact

Abstract Background: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major countries such as China, USA, Italy, France and the United Kingdom. We present outcome (’recovered’, ’isolated’ or ’death’) risk estimates of 2019-nCoV over ’early’ datasets. A major consideration is the likelihood of death for patients with 2019-nCoV.Method: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we used machine learning techniques (AdaBoost, bagging, extra-trees, decision trees and k-nearest Neighbour classifiers) on two 2019-nCoV datasets obtained from Kaggle on March 30, 2020. We used ’country’, ’age’ and ’gender’ as features to predict outcome for both datasets. We included the patient’s ’disease’ history (only present in the second dataset) to predict the outcome for the second dataset.Results: The use of a patient’s ’disease’ history improves the prediction of ’death’ by more than 7-fold. The models ignoring a patent’s ’disease’ history performed poorly in test predictions.Conclusion: Our findings indicate the potential of using a patient’s ’disease’ history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This development can have a positive effect on predictive patient treatment and can result in easing currently overburdened healthcare systems worldwide, especially with the increasing prevalence of second and third wave re-infections in some countries.

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Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

Journal of Medical Systems ◽

10.1007/s10916-020-01582-x ◽

2020 ◽

Vol 44 (7) ◽

Cited By ~ 19

Author(s):

A. S. Albahri ◽

Rula A. Hamid ◽

Jwan k. Alwan ◽

Z.T. Al-qays ◽

A. A. Zaidan ◽

...

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Data Mining ◽

Biological Data ◽

Machine Learning Techniques ◽

The Novel ◽

Learning Techniques ◽

Novel Coronavirus

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Maintaining proper health records improves machine learning predictions for novel 2019-nCoV

10.21203/rs.3.rs-33551/v1 ◽

2020 ◽

Author(s):

KOFFKA KHAN ◽

Emilie Ramsahai

Keyword(s):

Machine Learning ◽

Reporting Rate ◽

Machine Learning Techniques ◽

Patient Treatment ◽

Third Wave ◽

The United Kingdom ◽

Learning Techniques ◽

And Gender ◽

Novel Coronavirus ◽

The Impact

Abstract Background: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major cities such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of the 2019-nCoV over 'early' datasets. A major consideration is how likely are people to die from 2019-nCoV? Method: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we modelled machine learning techniques (AdaBoost, Bagging, Extra-Trees, Decision-Trees and k-Nearest Neighbours Classifiers) on two 2019-nCoV datasets obtained from Kaggle in March 30th 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. Including the patient's 'disease' history (only present in the second dataset) to predict outcome for the second dataset. Results: The use of a patient's 'disease' history improves the prediction of 'death' by more than a 7-fold. Models ignoring a patent's 'disease' history performed poorly in test predictions. Conclusion: Our findings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This can have a positive effect on predictive patient treatment and result in ease for current overburdened healthcare systems worldwide, especially with an increasing prevalence of second and third wave re-infections in some countries.

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Near real-time air quality forecasts using the NASA GEOS model

10.5194/egusphere-egu21-13587 ◽

2021 ◽

Author(s):

K. Emma Knowland ◽

Christoph Keller ◽

Krzysztof Wargan ◽

Brad Weir ◽

Pamela Wales ◽

...

Keyword(s):

Machine Learning ◽

Air Quality ◽

Real Time ◽

Weather Forecasting ◽

Atmospheric Composition ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Wide Range ◽

Reactive Trace Gases ◽

Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

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Automated Soil Residue Levels Detecting Device With IoT Interface

Advances in Data Mining and Database Management - Challenges and Applications of Data Analytics in Social Perspectives ◽

10.4018/978-1-7998-2566-1.ch007 ◽

2021 ◽

pp. 123-135

Author(s):

Adiraju Prasanth Rao ◽

K. Sudheer Reddy ◽

Sathiyamoorthi V.

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

The Novel ◽

Cloud Environment ◽

Application System ◽

Learning Techniques ◽

Social And Economic Development ◽

Residue Levels ◽

Soil Residue ◽

Agricultural Farm

Cloud computing and internet of things (IoT) are playing a crucial role in the present era of technological, social, and economic development. The novel models where cloud and IoT are integrated together are foreseen as disruptive and enable a number of application scenarios. The e-smart is an application system designed by leveraging cloud, IoT, and several other technology frameworks that are deployed on the agricultural farm to collect the data from the farm fields. The application extracts and collects the information about the residue levels of soil and crop details and the same data will be hosted in the cloud environment. The proposed e-smart application system is to analyze, integrate, and correlate datasets and produce decision-oriented reports to the farmer by using several machine learning techniques.

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Machine learning analysis of lifeguard flag decisions and recorded rescues

Natural Hazards and Earth System Science ◽

10.5194/nhess-19-2541-2019 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2541-2549

Author(s):

Chris Houser ◽

Jacob Lehner ◽

Nathan Cherry ◽

Phil Wernette

Keyword(s):

Machine Learning ◽

The United States ◽

Machine Learning Techniques ◽

Public Health Issue ◽

Rip Currents ◽

Effective Strategies ◽

Wave Forcing ◽

Learning Techniques ◽

Yellow Flag ◽

The Impact

Abstract. Rip currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags, and signs are important, and to varying degrees they are effective strategies to minimize risk to beach users. In the United States and other jurisdictions around the world, lifeguards use coloured flags (green, yellow, and red) to indicate whether the danger posed by the surf and rip hazard is low, moderate, or high respectively. The choice of flag depends on the lifeguard(s) monitoring the changing surf conditions along the beach and over the course of the day using both regional surf forecasts and careful observation. There is a potential that the chosen flag is not consistent with the beach user perception of the risk, which may increase the potential for rescues or drownings. In this study, machine learning is used to determine the potential for error in the flags used at Pensacola Beach and the impact of that error on the number of rescues. Results of a decision tree analysis indicate that the colour flag chosen by the lifeguards was different from what the model predicted for 35 % of days between 2004 and 2008 (n=396/1125). Days when there is a difference between the predicted and posted flag colour represent only 17 % of all rescue days, but those days are associated with ∼60 % of all rescues between 2004 and 2008. Further analysis reveals that the largest number of rescue days and total number of rescues are associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model predicted a green flag would be more appropriate based on the wind and wave forcing alone. While it is possible that the lifeguards were overly cautious, it is argued that they most likely identified a rip forced by a transverse-bar and rip morphology common at the study site. Regardless, the results suggest that beach users may be discounting lifeguard warnings if the flag colour is not consistent with how they perceive the surf hazard or the regional forecast. Results suggest that machine learning techniques have the potential to support lifeguards and thereby reduce the number of rescues and drownings.

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