Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition

Wang, Yongbin; Xu, Chunjie; Yao, Sanqiao; Wang, Lei; Yi, Zhao; Ren, Jun; Li, Yuchun

doi:10.1038/s41598-021-00948-6

Cited by 8 publications

(7 citation statements)

References 61 publications

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“…In addition to this, the ANFISE boosted the performance of FFNN, ANFIS, SVM, and MLR by 13, 6.1, 13.9, and 19.3 per cent, respectively. These numbers show that the capacity for the prediction of COVID-19 was increased in the case of the ensemble models rather than the single models, and these findings were compared to the findings of studies conducted in different fields using AI ensemble models [ 6 , 14 , 35 , 37 ]. Hence, these findings showed that ensemble models can be applied to the prediction of COVID-19 in the eastern Africa region more effectively than the single AI-driven models.…”

Section: Resultsmentioning

confidence: 85%

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Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa

Abegaz

Etikan

2022

Diagnostics

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East Africa was not exempt from the devastating effects of COVID-19, which led to the nearly complete cessation of social and economic activities worldwide. The objective of this study was to predict mortality due to COVID-19 using an artificial intelligence-driven ensemble model in East Africa. The dataset, which spans two years, was divided into training and verification datasets. To predict the mortality, three steps were conducted, which included a sensitivity analysis, the modelling of four single AI-driven models, and development of four ensemble models. Four dominant input variables were selected to conduct the single models. Hence, the coefficients of determination of ANFIS, FFNN, SVM, and MLR were 0.9273, 0.8586, 0.8490, and 0.7956, respectively. The non-linear ensemble approaches performed better than the linear approaches, and the ANFIS ensemble was the best-performing ensemble approach that boosted the predicting performance of the single AI-driven models. This fact revealed the promising capability of ensemble models for predicting the daily mortality due to COVID-19 in other parts of the globe.

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Section: Resultsmentioning

confidence: 85%

“…These ensemble techniques have been applied in various studies for purposes such as the clustering and classifications of medical data, web ranking, economic forecasting, etc. [ 34 , 35 , 36 , 37 , 38 , 39 ]. Considering this situation, this study also applied the ensemble technique to predict COVID-19 mortality in East Africa.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa

Abegaz

Etikan

2022

Diagnostics

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“…In the third step, different white noise series are used to repeat the first and second steps, and the results are added to the original time series each time. Finally, the set of IMF items in the EMD method is averaged ( 14 ).…”

Section: Methodsmentioning

confidence: 99%

Water Transmission Increases the Intensity of COVID-19 Outbreaks

Huang

Lian

Zhao

et al. 2022

Front. Public Health

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India suffered from a devastating 2021 spring outbreak of coronavirus disease 2019 (COVID-19), surpassing any other outbreaks before. However, the reason for the acceleration of the outbreak in India is still unknown. We describe the statistical characteristics of infected patients from the first case in India to June 2021, and trace the causes of the two outbreaks in a complete way, combined with data on natural disasters, environmental pollution and population movements etc. We found that water-to-human transmission accelerates COVID-19 spreading. The transmission rate is 382% higher than the human-to-human transmission rate during the 2020 summer outbreak in India. When syndrome coronavirus 2 (SARS-CoV-2) enters the human body directly through the water-oral transmission pathway, virus particles and nitrogen salt in the water accelerate viral infection and mutation rates in the gastrointestinal tract. Based on the results of the attribution analysis, without the current effective interventions, India could have experienced a third outbreak during the monsoon season this year, which would have increased the severity of the disaster and led to a South Asian economic crisis.

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“…Although the above EEMD-based prediction methods can improve prediction accuracy, the use of simple linear or non-linear models in the choice of prediction methods still leads to the omission of the characteristic laws implied by the component series. For this, Wang et al [43] used a non-linear autoregressive artificial neural network (NARANN) to model each IMF term of COVID-19 prevalence and mortality decomposed by EEMD, and an autoregressive integrated moving average model (ARIMA) to model the residual term to capture the non-linear and linear features of the component series, respectively, which can better fit the dynamic dependence of the epidemic time series. Thus, motivated by the EEMD-based decomposition-integration idea, capturing the features implied by the component sequences with an appropriate hybrid model is an available way to improve the model's performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction

Ke,

2024

Mathematics

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Due to the non-linear and non-stationary nature of daily new 2019 coronavirus disease (COVID-19) case time series, existing prediction methods struggle to accurately forecast the number of daily new cases. To address this problem, a hybrid prediction framework is proposed in this study, which combines ensemble empirical mode decomposition (EEMD), fuzzy entropy (FE) reconstruction, and a CNN-LSTM-ATT hybrid network model. This new framework, named EEMD-FE-CNN-LSTM-ATT, is applied to predict the number of daily new COVID-19 cases. This study focuses on the daily new case dataset from the United States as the research subject to validate the feasibility of the proposed prediction framework. The results show that EEMD-FE-CNN-LSTM-ATT outperforms other baseline models in all evaluation metrics, demonstrating its efficacy in handling the non-linear and non-stationary epidemic time series. Furthermore, the generalizability of the proposed hybrid framework is validated on datasets from France and Russia. The proposed hybrid framework offers a new approach for predicting the COVID-19 pandemic, providing important technical support for future infectious disease forecasting.

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Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition

Cited by 8 publications

References 61 publications

Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa

Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa

Water Transmission Increases the Intensity of COVID-19 Outbreaks

Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction

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