Interpretable Temporal Attention Network for COVID-19 forecasting

Zhou, Binggui; Yang, Guanghua; Shi, Zheng; Ma, Shaodan

doi:10.1016/j.asoc.2022.108691

Cited by 28 publications

(6 citation statements)

References 24 publications

(37 reference statements)

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“…Result as obtained by [19] indicated that the hybridized VMD artificial intelligence forecasting models outperformed the single forecasting models. Other predictive models such as the Bayesian sequential data assimilation [20] , regression [21] , ARIMA models [22] , combination of regression, ARIMA and Machine Learning models [23] , Interpretable Temporal Attention Network [24] , Artificial Neural Network [25] have also been applied to Covid-19 data. Similarly, state neural based framework [26] , ensemble learning models coupled with urban mobility information [27] , space time ARIMA [28] among other predictive models have been proposed for forecasting cases of Covid-19.…”

Section: Introductionmentioning

confidence: 99%

Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models

Busari

Samson²

2022

Scientific African

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Section: Introductionmentioning

confidence: 99%

Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models

Busari

Samson²

2022

Scientific African

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“…The proposed STAN model combines Graph Attention Network (GAT) and GRU and performs multi-period prediction using dynamic and static data as input. Zhou et al [ 43 ] proposed an Interpretable Temporal Attention Network (ITANet) to overcome the limitations of the existing deep learning-based COVID-19 prediction. The proposed ITANet consists of an encoder–decoder structure model and a Covariate Forecasting Network (CFN) model to reflect covariates.…”

Section: Related Researchmentioning

confidence: 99%

A Novel Approach on Deep Learning—Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs’ Effect over COVID-19 Pandemic

Jang

Kim

Lee

et al. 2022

IJERPH

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Following the outbreak of the COVID-19 pandemic, the continued emergence of major variant viruses has caused enormous damage worldwide by generating social and economic ripple effects, and the importance of PHSMs (Public Health and Social Measures) is being highlighted to cope with this severe situation. Accordingly, there has also been an increase in research related to a decision support system based on simulation approaches used as a basis for PHSMs. However, previous studies showed limitations impeding utilization as a decision support system for policy establishment and implementation, such as the failure to reflect changes in the effectiveness of PHSMs and the restriction to short-term forecasts. Therefore, this study proposes an LSTM-Autoencoder-based decision support system for establishing and implementing PHSMs. To overcome the limitations of existing studies, the proposed decision support system used a methodology for predicting the number of daily confirmed cases over multiple periods based on multiple output strategies and a methodology for rapidly identifying varies in policy effects based on anomaly detection. It was confirmed that the proposed decision support system demonstrated excellent performance compared to models used for time series analysis such as statistical models and deep learning models. In addition, we endeavored to increase the usability of the proposed decision support system by suggesting a transfer learning-based methodology that can efficiently reflect variations in policy effects. Finally, the decision support system proposed in this study provides a methodology that provides multi-period forecasts, identifying variations in policy effects, and efficiently reflects the effects of variation policies. It was intended to provide reasonable and realistic information for the establishment and implementation of PHSMs and, through this, to yield information expected to be highly useful, which had not been provided in the decision support systems presented in previous studies.

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“…Zhou et al [ 29 ] discussed the COVID-19 forecasting, using the relevance of government initiatives in their suggested model, the Interpretable Temporal Attention Network (ITANet). Long short-term memory (LSTM) for temporal feature extraction and multi-head attention for the long-term dependency caption are used in the proposed model, which has an encoder–decoder architecture.…”

Section: Background Review and Related Workmentioning

confidence: 99%

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

Tsan

Kristiani

Liu

et al. 2022

IJERPH

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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 Learning. We used Delhi, India, for daily confirmed cases and air pollutant data for the dataset. We used LSTM deep learning for training the combination of COVID-19 Confirmed Case and AQI parameters over the four different lag times of 1, 3, 7, and 14 days. The finding indicates that CO is the most excellent model compared with the others, having on average, 13 RMSE values. This was followed by pressure at 15, PM2.5 at 20, NO2 at 20, and O3 at 22 error rates.

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Interpretable Temporal Attention Network for COVID-19 forecasting

Cited by 28 publications

References 24 publications

Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models

Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models

A Novel Approach on Deep Learning—Based Decision Support System Applying Multiple Output LSTM-Autoencoder: Focusing on Identifying Variations by PHSMs’ Effect over COVID-19 Pandemic

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

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