Novel deep learning approach to model and predict the spread of COVID-19

Ayris, Devante; Imtiaz, Maleeha; Horbury, Kye; Williams, B.; Blackney, Mitchell; See, Celine Shi Hui; Shah, Syed Afaq Ali

doi:10.1016/j.iswa.2022.200068

Cited by 14 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The mean of the ten-day prediction MSE reached 0.169 for the EXP4 and only 0.272 for the mean of the EXP10, which further confirms the previous conclusion. The MAE is commonly used to assess the deviation between the predicted and actual SAPEI values; it is calculated in a way that avoids the influence of some extreme values on the overall results, making the results more stable [57][58][59]. Figure 4 shows that the MAE of the test set gradually rises as the prediction length increases.…”

Section: Identifying Key Variables To Enhance Prediction Accuracymentioning

confidence: 99%

Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China

Chen,

Wang,

Wei

et al. 2024

Atmosphere

View full text Add to dashboard Cite

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions.

show abstract

Section: Identifying Key Variables To Enhance Prediction Accuracymentioning

confidence: 99%

Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China

Chen,

Wang,

Wei

et al. 2024

Atmosphere

View full text Add to dashboard Cite

show abstract

“…So, these linear ARIMA and ES models are not suitable to capture the nonlinear dynamic marginal effect of the interaction of temperature and relative humidity. Nonlinear relationships between predictors (input variables) and the response (output variable) can be captured by supervised learning methods such as deep neural network (DNN), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost) ( Khan et al, 2021 , Hssayeni et al, 2021 , Zeroual et al, 2020 , Ayris et al, 2022 , Laatifi et al, 2022 ). Thus for a better prediction of the growth of an epidemic we apply supervised learning methods with a set of input features generated from the meteorological covariates and government intervention measures.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep neural network for monitoring the growth of COVID-19 epidemic using meteorological covariates

Khan

Chowdhury

Imon

2023

Intelligent Systems with Applications

View full text Add to dashboard Cite

“…They developed the Improved Particle Swarm Optimization (IPSO) algorithm, which introduced a generalized opposition-based learning strategy and an adaptive strategy to optimize the hyperparameters of the DNN. Ayris et al [165] proposed a deep sequential prediction model and a machine-learningbased nonparametric regression model to predict the transmission of COVID-19.…”

Section: Propagation Predictionmentioning

confidence: 99%

Advances in the Development of Representation Learning and Its Innovations against COVID-19

Li,

Parvej,

Zhang

et al. 2023

COVID

View full text Add to dashboard Cite

In bioinformatics research, traditional machine-learning methods have demonstrated efficacy in addressing Euclidean data. However, real-world data often encompass non-Euclidean forms, such as graph data, which contain intricate structural patterns or high-order relationships that elude conventional machine-learning approaches. Representation learning seeks to derive valuable data representations from enhancing predictive or analytic tasks, capturing vital patterns and structures. This method has proven particularly beneficial in bioinformatics and biomedicine, as it effectively handles high-dimensional and sparse data, detects complex biological patterns, and optimizes predictive performance. In recent years, graph representation learning has become a popular research topic. It involves the embedding of graphs into a low-dimensional space while preserving the structural and attribute information of the graph, enabling better feature extraction for downstream tasks. This study extensively reviews representation learning advancements, particularly in the research of representation methods since the emergence of COVID-19. We begin with an analysis and classification of neural-network-based language model representation learning techniques as well as graph representation learning methods. Subsequently, we explore their methodological innovations in the context of COVID-19, with a focus on the domains of drugs, public health, and healthcare. Furthermore, we discuss the challenges and opportunities associated with graph representation learning. This comprehensive review presents invaluable insights for researchers as it documents the development of COVID-19 and offers experiential lessons to preempt future infectious diseases. Moreover, this study provides guidance regarding future bioinformatics and biomedicine research methodologies.

show abstract

Novel deep learning approach to model and predict the spread of COVID-19

Cited by 14 publications

References 20 publications

Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China

Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China

Deep neural network for monitoring the growth of COVID-19 epidemic using meteorological covariates

Advances in the Development of Representation Learning and Its Innovations against COVID-19

Contact Info

Product

Resources

About