ArticleChaos, solitons, and fractals2020
A novel adaptive deep learning model of Covid-19 with focus on mortality reduction strategies.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 58 citations in OpenAlex.
- Optimal resource allocation model for COVID-19: a systematic review and meta-analysis.BMC infectious diseases · 2024Pooled it
- Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- EQISP: efficient quantized image signal processing with multi-scale pyramid fusion for resource constrained embodied perception.Frontiers in neurorobotics · 2026Article
- Deep learning infused SIRVD model for COVID-19 prediction: XGBoost-SIRVD-LSTM approach.Frontiers in medicine · 2024Article
- Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.Artificial intelligence in medicine · 2022Review
- Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis.Environmental science and pollution research international · 2022Article
- Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.Expert systems · 2022Article
- SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD.Computers in biology and medicine · 2021Article
- A simulated measurement for COVID-19 pandemic using the effective reproductive number on an empirical portion of population: epidemiological models.Neural computing & applications · 2021Article
- Predictive models for COVID-19 cases, deaths and recoveries in Algeria.Results in physics · 2021Article
- COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review.Interdisciplinary sciences, computational life sciences · 2021Review
- A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data.IEEE access : practical innovations, open solutions · 2021Article
- Intelligent system for COVID-19 prognosis: a state-of-the-art survey.Applied intelligence (Dordrecht, Netherlands) · 2021Article
- Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases.SN computer science · 2021Article
- Applications of artificial intelligence in battling against covid-19: A literature review.Chaos, solitons, and fractals · 2021Review
- A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic.Chaos, solitons, and fractals · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We employ deep learning to propose an Artificial Neural Network (ANN) based and data stream guided real-time incremental learning algorithm for parameter estimation of a non-intrusive, intelligent, adaptive and online analytical model of Covid-19 disease. Modeling and simulation of such problems pose an additional challenge of continuously evolving training data in which the model parameters change over time depending upon external factors. Our main contribution is that in a scenario of continuously evolving training data, unlike typical deep learning techniques, this non-intrusive algorithm eliminates the need to retrain or rebuild the model from scratch every time a new training data set is received. After validating the model, we use it to study the impact of different strategies for epidemic control. Finally, we propose and simulate a strategy of controlled natural immunization through risk-based population compartmentalization (PC) wherein the population is divided in Low Risk (LR) and High Risk (HR) compartments based on risk factors (like comorbidities and age) and subjected to different disease transmission dynamics by isolating the HR compartment while allowing the LR compartment to develop natural immunity. Upon release from the preventive isolation, the HR compartment finds itself surrounded by enough number of immunized individuals to prevent the spread of infection and thus most of the deaths occurring in this group are avoided.
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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.