ArticleExpert systems with applications2022
Temporal deep learning architecture for prediction of COVID-19 cases in India.
Article in Expert systems with applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Enhancing COVID-19 forecasts with a lightweight multi-head depthwise separable convolution network.Scientific reports · 2026Article
- CoViNAR: a context-aware social media dataset for pandemic severity level prediction and analysis.Frontiers in artificial intelligence · 2025Article
- Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries.Expert systems with applications · 2023Article
- Article
- STG-Net: A COVID-19 prediction network based on multivariate spatio-temporal information.Biomedical signal processing and control · 2023Article
- Multivariate time series short term forecasting using cumulative data of coronavirus.Evolving systems · 2023Article
- Forecasting COVID-19 spreading through an ensemble of classical and machine learning models: Spain's case study.Scientific reports · 2023Article
- Article
- Time series forecasting of COVID-19 infections and deaths in Alpha and Delta variants using LSTM networks.PloS one · 2023Article
- Article
- Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network.Applied soft computing · 2023Article
- COVID-19 contagion forecasting framework based on curve decomposition and evolutionary artificial neural networks: A case study in Andalusia, Spain.Expert systems with applications · 2022Article
- COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level.Sensors (Basel, Switzerland) · 2022Article
- Machine learning applications for COVID-19 outbreak management.Neural computing & applications · 2022Review
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3 authors.
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Abstract
To combat the recent coronavirus disease 2019 (COVID-19), academician and clinician are in search of new approaches to predict the COVID-19 outbreak dynamic trends that may slow down or stop the pandemic. Epidemiological models like Susceptible-Infected-Recovered (SIR) and its variants are helpful to understand the dynamics trend of pandemic that may be used in decision making to optimize possible controls from the infectious disease. But these epidemiological models based on mathematical assumptions may not predict the real pandemic situation. Recently the new machine learning approaches are being used to understand the dynamic trend of COVID-19 spread. In this paper, we designed the recurrent and convolutional neural network models: vanilla LSTM, stacked LSTM, ED_LSTM, BiLSTM, CNN, and hybrid CNN+LSTM model to capture the complex trend of COVID-19 outbreak and perform the forecasting of COVID-19 daily confirmed cases of 7, 14, 21 days for India and its four most affected states (Maharashtra, Kerala, Karnataka, and Tamil Nadu). The root mean square error (RMSE) and mean absolute percentage error (MAPE) evaluation metric are computed on the testing data to demonstrate the relative performance of these models. The results show that the stacked LSTM and hybrid CNN+LSTM models perform best relative to other models.
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