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

Rahmadani, Firda; Lee, Hyun Soo

doi:10.3390/app10238539

Cited by 17 publications

(13 citation statements)

References 41 publications

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“…However, alternative formulations could have been chosen. For example, Maher Ala’raj et al [ 27 ] coupled an ARIMA model, a very popular ML model for time series forecasting with a SEIRD model; Watson et al [ 17 ] embedded a Bayesian time series model and a random forest algorithm within a SIRD model; Rahmadani and Lee [ 28 ] combined a deep-learning algorithm with a SEIR model.…”

Section: Discussionmentioning

confidence: 99%

A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics

et al. 2022

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Anticipating intensive care unit (ICU) occupancy is critical in supporting decision makers to impose (or relax) measures that mitigate COVID-19 transmission. Mechanistic approaches such as Susceptible-Infected-Recovered (SIR) models have traditionally been used to achieve this objective. However, formulating such models is challenged by the necessity to formulate equations for plausible causal mechanisms between the intensity of COVID-19 transmission and external epidemic drivers such as temperature, and the stringency of non-pharmaceutical interventions. Here, we combined a neural network model (NN) with a Susceptible-Exposed-Infected-Recovered model (SEIR) in a hybrid model and attempted to increase the prediction accuracy of existing models used to forecast ICU occupancy. Between 1st of October, 2020 - 1st of July, 2021, the hybrid model improved performances of the SEIR model at different geographical levels. At a national level, the hybrid model improved, prediction accuracy (i.e., mean absolute error) by 74%. At the cantonal and hospital levels, the reduction on the forecast’s mean absolute error were 46% and 50%, respectively. Our findings illustrate those predictions from hybrid model can be used to anticipate occupancy in ICU, and support the decision-making for lifesaving actions such as the transfer of patients and dispatching of medical personnel and ventilators.

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Section: Discussionmentioning

confidence: 99%

A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics

et al. 2022

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“…Rahmadani and Lee. [ 46 ] proposed a hybrid deep learning model with an LSTM model and ordinary differential equations to model the epidemic prediction framework of SARS-CoV-2. Lee et al [ 47 ] proposed a real-time hybrid deep learning architecture using an RNN and a general DNN to predict running safety for a high-speed train.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms

Bakht

Sharma

Park

et al. 2022

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Particulate matter (PM) of sizes less than 10 µm () and 2.5 µm () found in the environment is a major health concern. As PM is more prevalent in an enclosed environment, such as a subway station, this can have a negative impact on the health of commuters and staff. Therefore, it is essential to continuously monitor PM on underground subway platforms and control it using a subway ventilation control system. In order to operate the ventilation system in a predictive way, a credible prediction model for indoor air quality (IAQ) is proposed. While the existing deterministic methods require extensive calculations and domain knowledge, deep learning-based approaches showed good performance in recent studies. In this study, we develop an effective hybrid deep learning framework to forecast future and on a subway platform using past air quality data. This hybrid framework is an integration of several deep learning frameworks, namely, convolution neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN), and is called hybrid CNN-LSTM-DNN; it has the characteristics to capture temporal patterns and informative characteristics from the indoor and outdoor air quality parameters compared with the standalone deep learning models. The effectiveness of the proposed and forecasting framework is demonstrated using comparisons with the different existing deep learning models.

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“…This can be another deep learning framework or analytical model. Rahmadani and Lee [30] proposed a hybrid deep learning model with a long short-term memory (LSTM) model and ordinary differential equations (ODE). In this study, a deep neural network (DNN) and a recurrent neural network (RNN) are integrated.…”

Section: 9 X For Peer Review 12 Of 20mentioning

confidence: 99%

Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle

et al. 2021

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Train running safety is considered one of the key criteria for advanced highway trains and bogies. While a number of existing research studies have focused on its measurement and monitoring, this study proposes a new and effective train running a safety prediction framework. The wheel derail coefficient, wheel rate of load reduction, and wheel lateral pressure are considered the decision variables for the safety framework. Data for actual measured rail conditions and vibration-based signals are used as the input data. However, advanced trains and bogies are influenced more by their inertial structures and mechanisms than by railway conditions and external environments. In order to reflect their inertial influences, past data of output variables are used as recurrent data. The proposed framework shares advantages of a general deep neural network and a recurrent neural network. To prove the effectiveness of the proposed hybrid deep-learning framework, numerical analyses using an actual measured train-railway model and transit simulation are conducted and compared with the existing deep learning architectures.

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

Cited by 17 publications

References 41 publications

A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics

A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics

Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms

Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle

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