Investigating the Spread of Coronavirus Disease via Edge-AI and Air Pollution Correlation

Coronavirus Disease 19 (COVID-19) is a highly infectious viral disease affecting millions of people worldwide in 2020. Several studies have shown that COVID-19 results in a severe acute respiratory syndrome and may lead to death. In past research, a greater number of respiratory diseases has been caused by exposure to air pollution for long periods of time. This article investigates the spread of COVID-19 as a result of air pollution by applying linear regression in machine learning method based edge computing. The analysis in this investigation have been based on the death rates caused by COVID-19 as well as the region of death rates based on hazardous air pollution using data retrieved from the Copernicus Sentinel-5P satellite. The results obtained in the investigation prove that the mortality rate due to the spread of COVID-19 is 77% higher in areas with polluted air. This investigation also proves that COVID-19 severely affected 68% of the individuals who had been exposed to polluted air.

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A comprehensive evaluation of air pollution prediction improvement by a machine learning method

2015 IEEE International Conference on Service Operations And Logistics, And Informatics (SOLI) ◽

10.1109/soli.2015.7367615 ◽

2015 ◽

Cited By ~ 9

Author(s):

Xia Xi ◽

Zhao Wei ◽

Rui Xiaoguang ◽

Wang Yijie ◽

Bai Xinxin ◽

...

Keyword(s):

Machine Learning ◽

Air Pollution ◽

Comprehensive Evaluation ◽

Machine Learning Method ◽

Learning Method ◽

Air Pollution Prediction ◽

Pollution Prediction

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Evaluation of COVID-19 Pandemic in Six Asia-Pacific Countries using Data-Driven and Machine Learning Method Based on the Current Management and Interventions

10.21203/rs.3.rs-251813/v1 ◽

2021 ◽

Author(s):

Shuang Ao ◽

Wenbo Huang ◽

Dan Han ◽

Yuming Liu ◽

Shuang Liu ◽

...

Keyword(s):

Machine Learning ◽

Large Scale ◽

Data Driven ◽

Asia Pacific ◽

Machine Learning Method ◽

Learning Method ◽

Current Management ◽

Large Populations ◽

The Government ◽

Using Data

Abstract Background South-east Asia and Western Pacific countries have large populations and underreporting of Covid19, which pose challenges to the large-scale response. Methods Data-driven methods are used to evaluate the Government or society’s interventions and the situation of the COVID-19 pandemic, and machine learning method are used to forecast the trend of COVID-19 pandemic based on the current management and interventions. Results The results show that. India received low government response index scores in February, and the number of confirmed cases and active cases in September became quite high with large stock and the overall growth rate is higher than 1. The number of daily confirmed cases in Bangladesh, Japan and Philippines is low and on the decline, it is rising in Malaysia and Indonesia. The number of active cases in Bangladesh, Japan, India and Bangladesh has begun to decline, Malaysia and Indonesia is no sign of decline. Bangladesh, Japan and Philippines will be flat or moderating, while Malaysia and Indonesia will still have no slowdown momentum and the situation will be severe. Conclusions The results show that the existing management and interventions responses are effective, although they have room for improvement, and Malaysia and Indonesia need to be improved and strengthened.

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