Predictive models for COVID-19 cases, deaths and recoveries in Algeria

Lounis, Mohamed; Torrealba-Rodriguez, O.; Conde-Gutiérrez, R.A.

doi:10.1016/j.rinp.2021.104845

Cited by 7 publications

(7 citation statements)

References 28 publications

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“…These operations are performed using matrix multiplication and pointwise operations followed by nonlinear activation functions. This carefully orchestrated mechanism allows LSTMs to model complex sequences, making them suitable for various applications [ 58 , 93 ].…”

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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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: 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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“…Tamang et al. (2020) used an ANN to construct a forecasting model for confirmed and fatal COVID-19 cases in India, the USA, the UK, and France, whereas Lounis et al. (2021) developed an inverse ANN model to estimate COVID-19 cases, deaths, and recoveries in Algeria.…”

Section: Literature Reviewmentioning

confidence: 99%

Weather Conditions and COVID-19 Cases: Insights from the GCC Countries

Abu-Abdoun

Al‐Shihabi²

2022

Intelligent Systems with Applications

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“…Motallebi et al (2022) [13] examined the association between face-covering mandates and the COVID-19 mortality decline accross 44 countries in 2 continents, and found that the COVID-19 death rate is lower in countries with face-mask mandates than in countries without mandates. Lounis et al (2021) [12] made a comparison of four models: Gompertz, Logistic, Bertalanffy and inverse artificial neural network (ANNi), with the aim of predicting the number of COVID-19 cases, deaths and recoveries in Algeria. For predicting death cases, they showed that the Gompertz model obtained the best precision with a minimum error of 1.14%.…”

Section: Introductionmentioning

confidence: 99%

A negative binomial regression analysis for COVID-19 death cases in Senegal

NIANG¹,

DIOP²,

SECK³

et al. 2022

Int. J. Stat. Appl. Math.

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We consider the negative binomial regression model (NBRM) to identify explanatory factors of the number of daily COVID-19 death cases in Senegal. The data are obtained from daily cases of COVID-19 following press releases from the Ministry of Health and Social Action (MSAS) during the period from February 24, 2021 to March 3, 2022. The extracted data were analysed using descriptive statistics and NBRM. The results of analysis revealed that the number of severe cases has a significant positive impact on the number of death cases of COVID-19 in Senegal. Furthermore, the number of vaccinated persons has a small and negative effect on the number of deaths.

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Predictive models for COVID-19 cases, deaths and recoveries in Algeria

Cited by 7 publications

References 28 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

Weather Conditions and COVID-19 Cases: Insights from the GCC Countries

A negative binomial regression analysis for COVID-19 death cases in Senegal

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