Deep learning for Covid-19 forecasting: State-of-the-art review.

Kamalov, Firuz; Rajab, Khairan; Cherukuri, Aswani Kumar; Elnagar, Ashraf; Safaraliev, Murodbek

doi:10.1016/j.neucom.2022.09.005

Cited by 28 publications

(13 citation statements)

References 102 publications

(110 reference statements)

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“…Another study [83]) analyzed the spread of COVID-19 in the most affected Brazilian cities using hybrid and single ARIMA models, which integrated EEMD and ARIMA techniques. The results showed that the EEMD performed approximately 27% better than the single model [33,[47][48][49][53][54][55]71,74,106,[110][111][112][134][135][136][137][138][139][140][141][142][143].…”

Section: Plos Onementioning

confidence: 99%

“…Researchers have been working on automating DCNN designs for image classification and searches. [ 48 , 96 , 140 ] proposed a drone-based prediction system that uses a network to identify patients with COVID-19. This model is implemented in areas with no Internet or wireless connections, and is used to identify and sanitize infected cases.…”

Section: Literature Reviewmentioning

confidence: 99%

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Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

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The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron’s, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

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Section: Plos Onementioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

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“…To extract useful information from a vast volume of data for further applications or a deeper comprehension of the underlying flow mechanism, machine Learning offers a multitude of analytical techniques. Firuz Kamalov et al([38] - [39]) explored the applications of machine learning. In addition, machine learning algorithms can automatically be active flow management and optimization, which can also improve the flow of information.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Machine learning impact of radiative blood flow over a wedge in a time-dependent MHD Williamson fluid

2024

AMCS

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The main objective of this research is to examine the radiation effects of an unsteady MHD Williamson biofluid (Blood) over a wedge that interacts with thermophoresis diffusion and Brownian motion. The necessary prerequisites of partial differential equations (PDEs) ensure the development of suitable mathematical frameworks for momentum, energy, and concentration. These appropriate nonlinear PDEs are frequently transmuted into ordinary differential equations (ODEs) by implemented similarity transformation. The results of these ODEs have a significant impact on the BVP4C approach from the MATLAB package computational structures. The graphs and tabular data provided the various values for pertinent parameters on the non-dimensional velocity temperature, concentration profiles, and the numerical values of skin friction, Nusselt number, and Sherwood number were found and discussed in detail. A novel aspect of the research effort was the effective incorporation of multiple linear regression (MLR) employing machine learning (ML), a statistical technique to forecast the physical quantities for present numerical results with an accuracy of 95%. Finally, the response and predicted variables were verified using linear regression. The potential benefit of these outcomes is to develop novel therapeutic and diagnostic strategies for cancer treatment, as well as for a better understanding of medical problems and designing more effective drug delivery systems. In particular, for significant developments in computer technology and resources, the enormous computational cost of these simulations still keeps them from becoming a clinical tool. An additional benefit is that the outcomes showed acceptable congruence with the tangible findings of recent research and enlargements for future investigators.

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“…Numerous methodologies are available for predicting the future trajectory of an epidemic, leveraging diverse modeling approaches. Particularly, machine learning (ML) models [1][2][3] and especially deep learning (DL) models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) featuring Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) cells, and multivariate CNNs [4][5][6] , have emerged as highly prominent approaches for forecasting. Several studies use a combination of multiple data-centric approaches (e.g., a machine learning model with ARIMA or Prophet 7 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancing COVID-19 Forecasting Precision through the Integration of Compartmental Models, Machine Learning and Variants

Baccega,

Castagno,

Anta

et al. 2024

Preprint

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Predicting epidemic evolution is essential for informed decision-making and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detection, proactive intervention, and targeted preventive measures.This paper introduces Sybil, a framework that integrates machine learning and variant-aware compartmental models, leveraging a fusion of data-centric and analytic methodologies. To validate and evaluate Sybil’s forecasts, we employed COVID-19 data from two European countries. The dataset included the number of new and recovered cases, fatalities, and variant presence over time. We evaluate the forecasting precision of Sybil in periods in which there is a change in the trend of the pandemic evolution or a new variant appears. Results demonstrate that Sybil outperforms a conventional data-centric approach, being able to forecast accurately the changes in the trend, the magnitude of these changes, and the future prevalence of new variants.

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Deep learning for Covid-19 forecasting: State-of-the-art review.

Cited by 28 publications

References 102 publications

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Machine learning impact of radiative blood flow over a wedge in a time-dependent MHD Williamson fluid

Enhancing COVID-19 Forecasting Precision through the Integration of Compartmental Models, Machine Learning and Variants

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