Multi-Model Selection and Analysis for COVID-19

Ma, Nuri; Ma, Weiyuan; Li, Zhiming

doi:10.3390/fractalfract5030120

Cited by 15 publications

(9 citation statements)

References 35 publications

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“…The system uses a SIR model and an LSTM to depict the current state of the pandemic and estimate its course into the future with reasonable accuracy (33). The RMSE calculates the difference between the actual and fitted data, while the R shows the relationship between the model's output and the actual data (34). LSTM networks, polynomial neural networks, and neural networks may all be used to anticipate COVID-19 situations (35).…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

Zhao¹,

Sun

Liu

et al. 2022

Front. Public Health

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A major emphasis is the dissemination of COVID-19 across the country's many regions and provinces. Using the present COVID-19 pandemic as a guide, the researchers suggest a hybrid model architecture for analyzing and optimizing COVID-19 data during the complete country. The analysis of COVID-19's exploration and death rate uses an ARIMA model with susceptible-infectious-removed and susceptible-exposed-infectious-removed (SEIR) models. The logistic model's failure to forecast the number of confirmed diagnoses and the snags of the SEIR model's too many tuning parameters are both addressed by a hybrid model method. Logistic regression (LR), Autoregressive Integrated Moving Average Model (ARIMA), support vector regression (SVR), multilayer perceptron (MLP), Recurrent Neural Networks (RNN), Gate Recurrent Unit (GRU), and long short-term memory (LSTM) are utilized for the same purpose. Root mean square error, mean absolute error, and mean absolute percentage error are used to show these models. New COVID-19 cases, the number of quarantines, mortality rates, and the deployment of public self-protection measures to reduce the epidemic are all outlined in the study's findings. Government officials can use the findings to guide future illness prevention and control choices.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

Zhao¹,

Sun

Liu

et al. 2022

Front. Public Health

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“…In [48], the authors proposed a fractional SEIR-AHQ model to predict COVID-19 cases in Beijing, Chongqing, Tianjin, and Heilongjiang. They investigate the period from 22 January 2020 to 5 March 2020 based on the data of the first 10 (early stage), 20 (middlestage), and 30 (late-stage) days, respectively.…”

Section: Review Of Predictions Modelsmentioning

confidence: 99%

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

Shahin

Almotairi

2021

Fractal Fract

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The COVID-19 pandemic has widely spread with an increasing infection rate through more than 200 countries. The governments of the world need to record the confirmed infectious, recovered, and death cases for the present state and predict the cases. In favor of future case prediction, governments can impose opening and closing procedures to save human lives by slowing down the pandemic progression spread. There are several forecasting models for pandemic time series based on statistical processing and machine learning algorithms. Deep learning has been proven as an excellent tool for time series forecasting problems. This paper proposes a deep learning time-series prediction model to forecast the confirmed, recovered, and death cases. Our proposed network is based on an encoding–decoding deep learning network. Moreover, we optimize the selection of our proposed network hyper-parameters. Our proposed forecasting model was applied in Saudi Arabia. Then, we applied the proposed model to other countries. Our study covers two categories of countries that have witnessed different spread waves this year. During our experiments, we compared our proposed model and the other time-series forecasting models, which totaled fifteen prediction models: three statistical models, three deep learning models, seven machine learning models, and one prophet model. Our proposed forecasting model accuracy was assessed using several statistical evaluation criteria. It achieved the lowest error values and achieved the highest R-squared value of 0.99. Our proposed model may help policymakers to improve the pandemic spread control, and our method can be generalized for other time series forecasting tasks.

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“…The reason of this is the memory effects of the disease (dependence not only of the current state of infected people, but also from the situation in the past). Let us mention two publications in this area [19,20]. In the first one, a two-side fractional generalized SEIR model is proposed, and the key epidemiological parameters of COVID-19 pandemic in the United States are identified and ranked.…”

Section: Introductionmentioning

confidence: 99%

“…In the first one, a two-side fractional generalized SEIR model is proposed, and the key epidemiological parameters of COVID-19 pandemic in the United States are identified and ranked. In [20] different integer-order and fractional-order models are explored, and their performance with COVID-19 data in China is analyzed.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling and Short-Term Forecasting of the COVID-19 Epidemic in Bulgaria: SEIRS Model with Vaccination

et al. 2022

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Data from the World Health Organization indicate that Bulgaria has the second-highest COVID-19 mortality rate in the world and the lowest vaccination rate in the European Union. In this context, to find the crucial epidemiological parameters that characterize the ongoing pandemic in Bulgaria, we introduce an extended SEIRS model with time-dependent coefficients. In addition to this, vaccination and vital dynamics are included in the model. We construct an appropriate Cauchy problem for a system of nonlinear ordinary differential equations and prove that its unique solution possesses some biologically reasonable features. Furthermore, we propose a numerical scheme and give an algorithm for the parameters identification in the obtained discrete problem. We show that the found values are close to the parameters values in the original differential problem. Based on the presented analysis, we develop a strategy for short-term prediction of the spread of the pandemic among the host population. The proposed model, as well as the methods and algorithms for parameters identification and forecasting, could be applied to COVID-19 data in every single country in the world.

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Multi-Model Selection and Analysis for COVID-19

Cited by 15 publications

References 35 publications

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

Mathematical Modeling and Short-Term Forecasting of the COVID-19 Epidemic in Bulgaria: SEIRS Model with Vaccination

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