ArticlePloS one2022
Deep learning via LSTM models for COVID-19 infection forecasting in India.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 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
- Modeling and forecasting neonatal mortality in Ethiopia: a comparative study using statistical, machine learning, and deep learning approaches.Archives of public health = Archives belges de sante publique · 2026Article
- A COVID-19 prediction Model based on symptomatology google trends and its optimization.BMC infectious diseases · 2026Article
- Respiratory viral infections: when and where? A scoping review of spatiotemporal methods.Journal of global health · 2025Article
- Analysis of a mathematical model for malaria using data-driven approach.Scientific reports · 2025Article
- Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model.Scientific reports · 2025Article
- Using a multi-strain infectious disease model with physical information neural networks to study the time dependence of SARS-CoV-2 variants of concern.PLoS computational biology · 2025Article
- Multi-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.PLoS computational biology · 2025Article
- AutoML-Driven Insights into Patient Outcomes and Emergency Care During Romania's First Wave of COVID-19.Bioengineering (Basel, Switzerland) · 2024Article
- Geospatial analysis of contagious infection growth and cross-boundary transmission in non-vaccinated districts of North-East Indian states during the COVID-19 pandemic.The Lancet regional health. Southeast Asia · 2024Article
- MGLEP: Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data.Scientific reports · 2024Article
- Deep learning infused SIRVD model for COVID-19 prediction: XGBoost-SIRVD-LSTM approach.Frontiers in medicine · 2024Article
- A dynamic ensemble model for short-term forecasting in pandemic situations.PLOS global public health · 2024Article
- Machine learning algorithm for ventilator mode selection, pressure and volume control.PloS one · 2024Article
- An autoregressive integrated moving average and long short-term memory (ARIM-LSTM) hybrid model for multi-source epidemic data prediction.PeerJ. Computer science · 2024Article
- Advancing diabetes prediction with a progressive self-transfer learning framework for discrete time series data.Scientific reports · 2023Article
- STG-Net: A COVID-19 prediction network based on multivariate spatio-temporal information.Biomedical signal processing and control · 2023Article
- Estimate the incubation period of coronavirus 2019 (COVID-19).Computers in biology and medicine · 2023Article
- COVID-19 focused series: diagnosis and forecast of COVID-19.Journal of thoracic disease · 2023Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low; however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle.
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