ArticlePersonal and ubiquitous computing2023
A systematic approach for COVID-19 predictions and parameter estimation.
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 It has been retracted, and should not be counted.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 12 papers.
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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
12 citing papers in PubMed.
- Predicting Covid-19 pandemic waves with biologically and behaviorally informed universal differential equations.Heliyon · 2024Article
- The value of artificial intelligence and imaging diagnosis in the fight against COVID-19.Personal and ubiquitous computing · 2023Article
- The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19.Personal and ubiquitous computing · 2023Article
- Deep learning modeling of public's sentiments towards temporal evolution of COVID-19 transmission.Applied soft computing · 2022Article
- Deming least square regressed feature selection and Gaussian neuro-fuzzy multi-layered data classifier for early COVID prediction.Expert systems · 2022Article
- Local binary pattern and deep learning feature extraction fusion for COVID-19 detection on computed tomography images.Expert systems · 2022Article
- Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques.Expert systems · 2022Article
- Seesaw scenarios of lockdown for COVID-19 pandemic: Simulation and failure analysis.Sustainable cities and society · 2021Article
- Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases.Expert systems · 2021Article
- Fake news detection for epidemic emergencies via deep correlations between text and images.Sustainable cities and society · 2021Article
- Modelling the Transmission Dynamics of COVID-19 in Six High-Burden Countries.BioMed research international · 2021Article
- What Can COVID-19 Teach Us about Using AI in Pandemics?Healthcare (Basel, Switzerland) · 2020Article
Corrections and comments
- Retraction · 2024-12-14Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Compromised Peer Review · Investigation by Journal/Publisher · Objections by Author(s) · Rogue Editor · Unreliable Results and/or Conclusions ·
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related issues to determine the maximum number of infected people, the speed of spread, and most importantly, its evaluation using a model-based parameter estimation method. In this research the Susceptible-Infectious-Recovered model with different conditions has been used for the analysis of COVID-19. The effects of lockdown, the light switch method, and parameter variations like contact ratio and reproduction number are also analyzed. The authors have attempted to study and predict the lockdown effect, particularly in India in terms of infected and recovered numbers, which show substantial improvement. A disease-free endemic stability analysis using Lyapunov and LaSalle's method is presented, and novel methods such as the convalescent plasma method and the Who Acquires Infection From Whom method are also discussed, as they are considered to be useful in flattening the curve of COVID-19.
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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.