ArticlePersonal and ubiquitous computing2023
Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks.
Article in Personal and ubiquitous computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 21 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 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
- Analysis of influenza-like illness trends in Saudi Arabia: a comparative study of statistical and deep learning techniques.Osong public health and research perspectives · 2025Article
- A prediction approach to COVID-19 time series with LSTM integrated attention mechanism and transfer learning.BMC medical research methodology · 2024Article
- AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.Osong public health and research perspectives · 2024Article
- An analysis of the waning effect of COVID-19 vaccinations.Genomics & informatics · 2023Article
- 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
- Assessment of Regional Health Resource Carrying Capacity and Security in Public Health Emergencies Based on the COVID-19 Outbreak.International journal of environmental research and public health · 2023Article
- Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.PloS one · 2023Article
- Using machine learning prediction models for quality control: a case study from the automotive industry.Computational management science · 2023Article
- Predicting COVID-19 using lioness optimization algorithm and graph convolution network.Soft computing · 2023Article
- Prediction and forecasting of worldwide corona virus (COVID-19) outbreak using time series and machine learning.Concurrency and computation : practice & experience · 2022Article
- Mathematical modeling of the impact of Omicron variant on the COVID-19 situation in South Korea.Genomics & informatics · 2022Article
- COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level.Sensors (Basel, Switzerland) · 2022Article
- A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer's Disease.Sensors (Basel, Switzerland) · 2022Article
- The effect of deep feature concatenation in the classification problem: An approach on COVID-19 disease detection.International journal of imaging systems and technology · 2022Article
- COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion.Sensors (Basel, Switzerland) · 2021Article
- Article
- A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images.Behavioural neurology · 2021Article
- A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification.Computational intelligence and neuroscience · 2021Article
- The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic.IEEE access : practical innovations, open solutions · 2021Article
Corrections and comments
- Retraction · 2025-11-07Concerns/Issues about Referencing/Attributions · Concerns/Issues about Results and/or Conclusions · Compromised Peer Review · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The novel human coronavirus disease COVID-19 has become the fifth documented pandemic since the 1918 flu pandemic. COVID-19 was first reported in Wuhan, China, and subsequently spread worldwide. Almost all of the countries of the world are facing this natural challenge. We present forecasting models to estimate and predict COVID-19 outbreak in Asia Pacific countries, particularly Pakistan, Afghanistan, India, and Bangladesh. We have utilized the latest deep learning techniques such as Long Short Term Memory networks (LSTM), Recurrent Neural Network (RNN), and Gated Recurrent Units (GRU) to quantify the intensity of pandemic for the near future. We consider the time variable and data non-linearity when employing neural networks. Each model's salient features have been evaluated to foresee the number of COVID-19 cases in the next 10 days. The forecasting performance of employed deep learning models shown up to July 01, 2020, is more than 90% accurate, which shows the reliability of the proposed study. We hope that the present comparative analysis will provide an accurate picture of pandemic spread to the government officials so that they can take appropriate mitigation measures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.