ODE-based Epidemic Network Simulation of Viral Hepatitis A and Kernel Support Vector Machine based Vaccination Effect Analysis

Rahmadani, Firda; Lee, Hyunsoo

doi:10.5391/jkiis.2020.30.2.106

Cited by 4 publications

(3 citation statements)

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“…The COVID-19 epidemic dynamics are predicted using a modified SEIR compartmental model (23). The dynamic process of epidemic spread is described using a simple SEIR model developed (24). The average age of the people of Quezon City is significantly lower than that of the most vulnerable demographic (22).…”

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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“…where γ is the influence on classification outcomes; large values for γ leads to overfitting and small values result in under-fitting [50].…”

Section: Clusteringmentioning

confidence: 99%

Predicting Physical Exercise Adherence in Fitness Apps Using a Deep Learning Approach

Jossa-Bastidas

Zahia

Fuente-Vidal

et al. 2021

IJERPH

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The use of mobile fitness apps has been on the rise for the last decade and especially during the worldwide SARS-CoV-2 pandemic, which led to the closure of gyms and to reduced outdoor mobility. Fitness apps constitute a promising means for promoting more active lifestyles, although their attrition rates are remarkable and adherence to their training plans remains a challenge for developers. The aim of this project was to design an automatic classification of users into adherent and non-adherent, based on their training behavior in the first three months of app usage, for which purpose we proposed an ensemble of regression models to predict their behaviour (adherence) in the fourth month. The study was conducted using data from a total of 246 Mammoth Hunters Fitness app users. Firstly, pre-processing and clustering steps were taken in order to prepare the data and to categorize users into similar groups, taking into account the first 90 days of workout sessions. Then, an ensemble approach for regression models was used to predict user training behaviour during the fourth month, which were trained with users belonging to the same cluster. This was used to reach a conclusion regarding their adherence status, via an approach that combined affinity propagation (AP) clustering algorithm, followed by the long short-term memory (LSTM), rendering the best results (87% accuracy and 85% F1_score). This study illustrates the suggested the capacity of the system to anticipate future adherence or non-adherence, potentially opening the door to fitness app creators to pursue advanced measures aimed at reducing app attrition.

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“…Previous studies [5][6][7] applied a modified SEIR compartmental model to predict the COVID-19 epidemic dynamics. This study incorporates a basic SEIR model [8] to describe the dynamic process of epidemic propagation. Given a population that contains a group of individuals, a compartment SEIR model includes four states at time t: susceptible (𝑆(𝑡)), exposed (𝐸(𝑡)), infected (𝐼(𝑡)), and recovered (𝑅(𝑡)), as shown in Figure 1.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case

Rahmadani

Lee

2020

Applied Sciences

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The emergence of COVID-19 and the pandemic have changed and devastated every aspect of our lives. Before effective vaccines are widely used, it is important to predict the epidemic patterns of COVID-19. As SARS-CoV-2 is transferred primarily by droplets of infected people, the incorporation of human mobility is crucial in epidemic dynamics models. This study expands the susceptible–exposed–infected–recovered compartment model by considering human mobility among a number of regions. Although the expanded meta-population epidemic model exhibits better performance than general compartment models, it requires a more accurate estimation of the extended modeling parameters. To estimate the parameters of these epidemic models, the meta-population model is incorporated with deep learning models. The combined deep learning model generates more accurate modeling parameters, which are used for epidemic meta-population modeling. In order to demonstrate the effectiveness of the proposed hybrid deep learning framework, COVID-19 data in South Korea were tested, and the forecast of the epidemic patterns was compared with other estimation methods.

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ODE-based Epidemic Network Simulation of Viral Hepatitis A and Kernel Support Vector Machine based Vaccination Effect Analysis

Cited by 4 publications

References 0 publications

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

Predicting Physical Exercise Adherence in Fitness Apps Using a Deep Learning Approach

Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case

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