ArticleApplied soft computing2022
Interpretable Temporal Attention Network for COVID-19 forecasting.
Article in Applied soft computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Predictive and interpretable machine learning for COVID-19 resurgences: the role of SARS-CoV-2 variants in the post-pandemic era.BMC infectious diseases · 2025Article
- Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer.PLoS computational biology · 2024Article
- Novel cost-effective method for forecasting COVID-19 and hospital occupancy using deep learning.Scientific reports · 2024Article
- Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022.BMC infectious diseases · 2024Article
- Article
- An analytical tool to support public policies and isolation barriers against SARS-CoV-2 based on mobility patterns and socio-economic aspects.Applied soft computing · 2023Article
- Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model.Chaos, solitons, and fractals · 2022Article
- Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models.Scientific African · 2022Article
- 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.International journal of environmental research and public health · 2022Article
- In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning.International journal of environmental research and public health · 2022Article
- On the accuracy of Covid-19 forecasting methods in Russia for two years.Procedia computer science · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The worldwide outbreak of coronavirus disease 2019 (COVID-19) has triggered an unprecedented global health and economic crisis. Early and accurate forecasts of COVID-19 and evaluation of government interventions are crucial for governments to take appropriate interventions to contain the spread of COVID-19. In this work, we propose the Interpretable Temporal Attention Network (ITANet) for COVID-19 forecasting and inferring the importance of government interventions. The proposed model is with an encoder-decoder architecture and employs long short-term memory (LSTM) for temporal feature extraction and multi-head attention for long-term dependency caption. The model simultaneously takes historical information, a priori known future information, and pseudo future information into consideration, where the pseudo future information is learned with the covariate forecasting network (CFN) and multi-task learning (MTL). In addition, we also propose the degraded teacher forcing (DTF) method to train the model efficiently. Compared with other models, the ITANet is more effective in the forecasting of COVID-19 new confirmed cases. The importance of government interventions against COVID-19 is further inferred by the Temporal Covariate Interpreter (TCI) of the model.
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