In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning

Tsan, Yu‐Tse; Kristiani, Endah; Liu, Po‐Yu; Chu, Wei‐Min; Yang, Chao-Tung

doi:10.3390/ijerph19116373

IJERPH

2022

DOI: 10.3390/ijerph19116373

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In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning

Yu‐Tse Tsan

Endah Kristiani

Po‐Yu Liu

et al.

Abstract: The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have also helped shape public health guidelines and direct resources; however, they are challenging to analyze and predict since those events still happen. This paper intends to invesitgate the association between air pollutants and COVID-19 confirmed cases using Deep Le… Show more

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Cited by 2 publications

References 42 publications

(44 reference statements)

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Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning

Ramirez-Alcocer

Tello-Leal²,

Macías-Hernández³

et al. 2022

Atmosphere

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Air pollution is associated with respiratory diseases and the transmission of infectious diseases. In this context, the association between meteorological factors and poor air quality possibly contributes to the transmission of COVID-19. Therefore, analyzing historical data of particulate matter (PM2.5, and PM10) and meteorological factors in indoor and outdoor environments to discover patterns that allow predicting future confirmed cases of COVID-19 is a challenge within a long pandemic. In this study, a hybrid approach based on machine learning and deep learning is proposed to predict confirmed cases of COVID-19. On the one hand, a clustering algorithm based on K-means allows the discovery of behavior patterns by forming groups with high cohesion. On the other hand, multivariate linear regression is implemented through a long short-term memory (LSTM) neural network, building a reliable predictive model in the training stage. The LSTM prediction model is evaluated through error metrics, achieving the highest performance and accuracy in predicting confirmed cases of COVID-19, using data of PM2.5 and PM10 concentrations and meteorological factors of the outdoor environment. The predictive model obtains a root-mean-square error (RMSE) of 0.0897, mean absolute error (MAE) of 0.0837, and mean absolute percentage error (MAPE) of 0.4229 in the testing stage. When using a dataset of PM2.5, PM10, and meteorological parameters collected inside 20 households from 27 May to 13 October 2021, the highest performance is obtained with an RMSE of 0.0892, MAE of 0.0592, and MAPE of 0.2061 in the testing stage. Moreover, in the validation stage, the predictive model obtains a very acceptable performance with values between 0.4152 and 3.9084 for RMSE, and a MAPE of less than 4.1%, using three different datasets with indoor environment values.

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Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning

Ramirez-Alcocer

Tello-Leal²,

Macías-Hernández³

et al. 2022

Atmosphere

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Hybrid Deep Learning-Based Air Pollution Prediction and Index Classification Using an Optimization Algorithm

Kutala,

Awari,

Velu

et al. 2024

AIMSES

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Lockdowns were implemented in nearly all countries in the world in order to reduce the spread of COVID-19. The majority of the production activities like industries, transportation, and construction were restricted completely. This unprecedented stagnation of resident's consumption and industrial production has efficiently reduced air pollution emissions, providing typical and natural test sites to estimate the effects of human activity controlling on air pollution control and reduction. Air pollutants impose higher risks on the health of human beings and also damage the ecosystem. Previous research has used machine learning (ML) and statistical modeling to categorize and predict air pollution. This study developed a binary spring search optimization with hybrid deep learning (BSSO-HDL) for air pollution prediction and an air quality index (AQI) classification process during the pandemic. At the initial stage, the BSSO-HDL model pre-processes the actual air quality data and makes it compatible for further processing. In the presented BSSO-HDL model, an HDL-based air quality prediction and AQI classification model was applied in which the HDL was derived by the use of a convolutional neural network with an extreme learning machine (CNN-ELM) algorithm. To optimally modify the hyperparameter values of the BSSO-HDL model, the BSSO algorithm-based hyperparameter tuning procedure gets executed. The experimental outcome demonstrates the promising prediction classification performance of the BSSO-HDL model. This model, developed on the Python platform, was evaluated using the coefficient of determination <italic>R</italic>2, the mean absolute error (MAE), and the root mean squared error (RMSE) error measures. With an <italic>R</italic>2 of 0.922, RMSE of 15.422, and MAE of 10.029, the suggested BSSO-HDL technique outperforms established models such as XGBoost, support vector machines (SVM), random forest (RF), and the ensemble model (EM). This demonstrates its ability in providing precise and reliable AQI predictions.

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In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning

Cited by 2 publications

References 42 publications

Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning

Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning

Hybrid Deep Learning-Based Air Pollution Prediction and Index Classification Using an Optimization Algorithm

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