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Han K, Lee B, Lee D, Heo G, Oh J, Lee S, Apio C, Park T. Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data. Sci Rep 2024; 14:9962. [PMID: 38693172 PMCID: PMC11063074 DOI: 10.1038/s41598-024-58835-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 04/03/2024] [Indexed: 05/03/2024] Open
Abstract
The COVID-19 pandemic caused by the novel SARS-COV-2 virus poses a great risk to the world. During the COVID-19 pandemic, observing and forecasting several important indicators of the epidemic (like new confirmed cases, new cases in intensive care unit, and new deaths for each day) helped prepare the appropriate response (e.g., creating additional intensive care unit beds, and implementing strict interventions). Various predictive models and predictor variables have been used to forecast these indicators. However, the impact of prediction models and predictor variables on forecasting performance has not been systematically well analyzed. Here, we compared the forecasting performance using a linear mixed model in terms of prediction models (mathematical, statistical, and AI/machine learning models) and predictor variables (vaccination rate, stringency index, and Omicron variant rate) for seven selected countries with the highest vaccination rates. We decided on our best models based on the Bayesian Information Criterion (BIC) and analyzed the significance of each predictor. Simple models were preferred. The selection of the best prediction models and the use of Omicron variant rate were considered essential in improving prediction accuracies. For the test data period before Omicron variant emergence, the selection of the best models was the most significant factor in improving prediction accuracy. For the test period after Omicron emergence, Omicron variant rate use was considered essential in deciding forecasting accuracy. For prediction models, ARIMA, lightGBM, and TSGLM generally performed well in both test periods. Linear mixed models with country as a random effect has proven that the choice of prediction models and the use of Omicron data was significant in determining forecasting accuracies for the highly vaccinated countries. Relatively simple models, fit with either prediction model or Omicron data, produced best results in enhancing forecasting accuracies with test data.
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Affiliation(s)
- Kyulhee Han
- Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Bogyeom Lee
- Department of Industrial Engineering, Seoul National University, Seoul, Republic of Korea
| | - Doeun Lee
- Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Gyujin Heo
- Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Jooha Oh
- Ross School of Business, University of Michigan-Ann Arbor, Ann Arbor, MI, United States
| | - Seoyoung Lee
- College of Humanities, Seoul National University, Seoul, Republic of Korea
| | - Catherine Apio
- Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Taesung Park
- Ross School of Business, University of Michigan-Ann Arbor, Ann Arbor, MI, United States.
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2
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Tariq MU, Ismail SB. AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods. Osong Public Health Res Perspect 2024; 15:115-136. [PMID: 38621765 PMCID: PMC11082441 DOI: 10.24171/j.phrp.2023.0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/07/2024] [Accepted: 01/26/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation's public health authorities in informed decision-making. METHODS This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. RESULTS The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. CONCLUSION This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.
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Affiliation(s)
- Muhammad Usman Tariq
- Marketing, Operations, and Information System, Abu Dhabi University, Abu Dhabi, United Arab Emirates
- Faculty of Computer Science and Information Technology, Univesiti Tun Hussien Onn Malaysia, Parit Raja, Malaysia
| | - Shuhaida Binti Ismail
- Faculty of Computer Science and Information Technology, Univesiti Tun Hussien Onn Malaysia, Parit Raja, Malaysia
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3
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Tariq MU, Ismail SB. Deep learning in public health: Comparative predictive models for COVID-19 case forecasting. PLoS One 2024; 19:e0294289. [PMID: 38483948 PMCID: PMC10939212 DOI: 10.1371/journal.pone.0294289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 10/28/2023] [Indexed: 03/17/2024] Open
Abstract
The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.
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Affiliation(s)
- Muhammad Usman Tariq
- Abu Dhabi University, Abu Dhabi, United Arab Emirates
- Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Malaysia
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4
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Liu M, Zheng H, Cai M, Leung KMY, Li Y, Yan M, Zhang Z, Zhang K, Chen M, Ke H. Ocean Stratification Impacts on Dissolved Polycyclic Aromatic Hydrocarbons (PAHs): From Global Observation to Deep Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18339-18349. [PMID: 37651694 DOI: 10.1021/acs.est.3c03237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Ocean stratification plays a crucial role in many biogeochemical processes of dissolved matter, but our understanding of its impact on widespread organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), remains limited. By analyzing dissolved PAHs collected from global oceans and marginal seas, we found different patterns in vertical distributions of PAHs in relation to ocean primary productivity and stratification index. Notably, a significant positive logarithmic relationship (R2 = 0.50, p < 0.05) was observed between the stratification index and the PAH stock. To further investigate the impact of ocean stratification on PAHs, we developed a deep learning neural network model. This model incorporated input variables determining the state of the seawater or the stock of PAHs. The modeled PAH stocks displayed substantial agreement with the observed values (R2 ≥ 0.92), suggesting that intensified stratification could prompt the accumulation of PAHs in the water column. Given the amplified effect of global warming, it is imperative to give more attention to increased ocean stratification and its impact on the environmental fate of organic pollutants.
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Affiliation(s)
- Mengyang Liu
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, College of the Environment and Ecology, Xiamen University, Xiamen 361102, P. R. China
- State Key Laboratory of Marine Pollution, and Department of Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, P. R. China
| | - Haowen Zheng
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, College of the Environment and Ecology, Xiamen University, Xiamen 361102, P. R. China
| | - Minggang Cai
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, College of the Environment and Ecology, Xiamen University, Xiamen 361102, P. R. China
| | - Kenneth M Y Leung
- State Key Laboratory of Marine Pollution, and Department of Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, P. R. China
| | - Yifan Li
- Harbin Institute of Technology, Harbin 150090, P. R. China
| | - Meng Yan
- State Key Laboratory of Marine Pollution, and Department of Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, P. R. China
| | - Zifeng Zhang
- Harbin Institute of Technology, Harbin 150090, P. R. China
| | - Kai Zhang
- State Key Laboratory of Marine Pollution, and Department of Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, P. R. China
| | - Meng Chen
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, College of the Environment and Ecology, Xiamen University, Xiamen 361102, P. R. China
| | - Hongwei Ke
- State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, College of the Environment and Ecology, Xiamen University, Xiamen 361102, P. R. China
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5
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de Souza GN, Mendes AGB, Costa JDS, Oliveira MDS, Lima PVC, de Moraes VN, Silva DCC, da Rocha JEC, Botelho MDN, Araujo FA, Fernandes RDS, Souza DL, Braga MDB. Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil. PLoS One 2023; 18:e0291138. [PMID: 37976312 PMCID: PMC10656034 DOI: 10.1371/journal.pone.0291138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/22/2023] [Indexed: 11/19/2023] Open
Abstract
Modeling time series has been a particularly challenging aspect due to the need for constant adjustments in a rapidly changing environment, data uncertainty, dependencies between variables, volatile fluctuations, and the need to identify ideal hyperparameters. The present study presents a Framework capable of making projections from time series related to cases and deaths by COVID-19 in the Amazonian state of Pará, in Brazil. For the first time, deep learning models such as TCN, TRANSFORMER, TFT, N-BEATS, and N-HiTS were assessed for this purpose. The ARIMA statistical model was also used in post-processing for residual adjustment and short-term smoothing of the generated forecasts. The Framework generates probabilistic forecasts, with multivariate support, considering the following variables: daily cases per day of the first symptom, cases published daily, the occurrence of deaths, deaths published daily, and percentage of daily vaccination. The generated predictions are statistically evaluated by determining the best model for 7-day moving average projections using evaluating metrics such as MSE, RMSE, MAPE, sMAPE, r2, Coefficient of Variation, and residual analysis. As a result, the generated projections showed an average error of 5.4% for Cases Publication, 8.0% for Cases Symptoms, 11.12% for Deaths Publication, and 4.6% for Deaths Occurrence, with the N-HiTS and N-BEATS models obtaining better results. In general terms, the use of deep learning models to predict cases and deaths from COVID-19 has proven to be a valuable practice for analyzing the spread of the virus, which allows health managers to better understand and respond to this kind of pandemic outbreak.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Daniel Leal Souza
- Computer Science Institute, Centro Universitário do Estado do Pará, Belém, Pará, Brazil
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6
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Abd Elaziz M, Dahou A, Mabrouk A, El-Sappagh S, Aseeri AO. An Efficient Artificial Rabbits Optimization Based on Mutation Strategy For Skin Cancer Prediction. Comput Biol Med 2023; 163:107154. [PMID: 37364532 DOI: 10.1016/j.compbiomed.2023.107154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/26/2023] [Accepted: 06/07/2023] [Indexed: 06/28/2023]
Abstract
Accurate skin lesion diagnosis is critical for the early detection of melanoma. However, the existing approaches are unable to attain substantial levels of accuracy. Recently, pre-trained Deep Learning (DL) models have been applied to tackle and improve efficiency on tasks such as skin cancer detection instead of training models from scratch. Therefore, we develop a robust model for skin cancer detection with a DL-based model as a feature extraction backbone, which is achieved using MobileNetV3 architecture. In addition, a novel algorithm called the Improved Artificial Rabbits Optimizer (IARO) is introduced, which uses the Gaussian mutation and crossover operator to ignore the unimportant features from those features extracted using MobileNetV3. The PH2, ISIC-2016, and HAM10000 datasets are used to validate the developed approach's efficiency. The empirical results show that the developed approach yields outstanding accuracy results of 87.17% on the ISIC-2016 dataset, 96.79% on the PH2 dataset, and 88.71 % on the HAM10000 dataset. Experiments show that the IARO can significantly improve the prediction of skin cancer.
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Affiliation(s)
- Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt; Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates; Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon; MEU Research Unit, Middle East University, Amman 11831, Jordan.
| | - Abdelghani Dahou
- Mathematics and Computer Science Department, University of Ahmed DRAIA, 01000, Adrar, Algeria.
| | - Alhassan Mabrouk
- Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni Suef 62511, Egypt.
| | - Shaker El-Sappagh
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Egypt; Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt.
| | - Ahmad O Aseeri
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
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7
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M RJ, G M, G B, P S. SVM-RFE enabled feature selection with DMN based centroid update model for incremental data clustering using COVID-19. Comput Methods Biomech Biomed Engin 2023:1-15. [PMID: 37485999 DOI: 10.1080/10255842.2023.2236744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/02/2023] [Accepted: 07/08/2023] [Indexed: 07/25/2023]
Abstract
This research introduces an efficacious model for incremental data clustering using Entropy weighted-Gradient Namib Beetle Mayfly Algorithm (NBMA). Here, feature selection is done based upon support vector machine recursive feature elimination (SVM-RFE), where the weight parameter is optimally fine-tuned using NBMA. After that, clustering is carried out utilizing entropy weighted power k-means clustering algorithm and weight is updated employing designed Gradient NBMA. Finally, incremental data clustering takes place in which centroid matching is carried out based on RV coefficient, whereas centroid is updated based on deep maxout network (DMN). Also, the result shows the better performance of the proposed method..
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Affiliation(s)
- Robinson Joel M
- Information Technology, Kings Engineering College, Sriperumbudur, India
| | - Manikandan G
- Information Technology, Kings Engineering College, Sriperumbudur, India
| | - Bhuvaneswari G
- Department of Computer Science and Engineering (Cyber Security), Saveetha Engineering College, Saveetha Nagar, Chennai, Tamil Nadu, India
| | - Shanthakumar P
- Information Technology, Kings Engineering College, Sriperumbudur, India
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8
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Tariq MU, Ismail SB, Babar M, Ahmad A. Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. PLoS One 2023; 18:e0287755. [PMID: 37471397 PMCID: PMC10359009 DOI: 10.1371/journal.pone.0287755] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 06/09/2023] [Indexed: 07/22/2023] Open
Abstract
The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population.
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Affiliation(s)
- Muhammad Usman Tariq
- Abu Dhabi University, Abu Dhabi, United Arab Emirates
- Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Malaysia
| | | | - Muhammad Babar
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, Saudi Arabia
| | - Ashir Ahmad
- College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
- Swinburne University of Technology, Melbourne, Australia
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9
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Chakraborty D, Goswami D, Ghosh S, Ghosh A, Chan JH, Wang L. Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks. Sci Rep 2023; 13:6795. [PMID: 37100806 PMCID: PMC10130813 DOI: 10.1038/s41598-023-31737-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/16/2023] [Indexed: 04/28/2023] Open
Abstract
The COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With a larger number of people getting affected during the second wave, hospitals were overburdened, running out of supplies and oxygen. Hence, predicting new COVID-19 cases, new deaths, and total active cases multiple days in advance can aid better utilization of scarce medical resources and prudent pandemic-related decision-making. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models pre-trained on COVID-19 data from four different countries (United States of America, Brazil, Spain, and Bangladesh) and fine-tuned on India's data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then gives 7-day ahead predictions using the recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the different models. This method with two countries, Spain and Bangladesh, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models.
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Affiliation(s)
| | - Debayan Goswami
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Susmita Ghosh
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
| | - Ashish Ghosh
- Technology Innovation Hub (TIH), Indian Statistical Institute, Kolkata, India
| | - Jonathan H Chan
- Innovative Cognitive Computing (IC2) Research Center, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
| | - Lipo Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
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10
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Yang H, Liu H, Li G. A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases. Comput Biol Med 2023; 156:106674. [PMID: 36871336 PMCID: PMC9942481 DOI: 10.1016/j.compbiomed.2023.106674] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 01/15/2023] [Accepted: 02/11/2023] [Indexed: 02/25/2023]
Abstract
Coronavirus disease (COVID-19) has infected billion people around the world and affected the economy, but most countries are considering reopening, so the COVID-19 daily confirmed and death cases have increased greatly. It is very necessary to predict the COVID-19 daily confirmed and death cases in order to help every country formulate prevention policies. To enhance the prediction performance, this paper proposes a prediction model based on improved variational mode decomposition by sparrow search algorithm (SVMD), improved kernel extreme learning machine by Aquila optimizer algorithm (AO-KELM) and error correction idea, named SVMD-AO-KELM-error for short-term prediction of COVID-19 cases. Firstly, to solve mode number and penalty factor selection of variational mode decomposition (VMD), an improved VMD based on sparrow search algorithm (SSA), named SVMD, is proposed. SVMD decomposes the COVID-19 case data into some intrinsic mode function (IMF) components and residual is considered. Secondly, to properly selected regularization coefficients and kernel parameters of kernel extreme learning machine (KELM) and improve the prediction performance of KELM, an improved KELM by Aquila optimizer (AO) algorithm, named AO-KELM, is proposed. Each component is predicted by AO-KELM. Then, the prediction error of IMF and residual are predicted by AO-KELM to correct prediction results, which is error correction idea. Finally, prediction results of each component and error prediction results are reconstructed to get final prediction results. Through the simulation experiment of the COVID-19 daily confirmed and death cases in Brazil, Mexico, and Russia and comparison with twelve comparative models, simulation experiment gives that SVMD-AO-KELM-error has best prediction accuracy. It also proves that the proposed model can be used to predict the pandemic COVID-19 cases and offers a novel approach for COVID-19 cases prediction.
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Affiliation(s)
- Hong Yang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China.
| | - Heng Liu
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China
| | - Guohui Li
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China.
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11
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Data driven contagion risk management in low-income countries using machine learning applications with COVID-19 in South Asia. Sci Rep 2023; 13:3732. [PMID: 36878910 PMCID: PMC9987367 DOI: 10.1038/s41598-023-30348-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/21/2023] [Indexed: 03/08/2023] Open
Abstract
In the absence of real-time surveillance data, it is difficult to derive an early warning system and potential outbreak locations with the existing epidemiological models, especially in resource-constrained countries. We proposed a contagion risk index (CR-Index)-based on publicly available national statistics-founded on communicable disease spreadability vectors. Utilizing the daily COVID-19 data (positive cases and deaths) from 2020 to 2022, we developed country-specific and sub-national CR-Index for South Asia (India, Pakistan, and Bangladesh) and identified potential infection hotspots-aiding policymakers with efficient mitigation planning. Across the study period, the week-by-week and fixed-effects regression estimates demonstrate a strong correlation between the proposed CR-Index and sub-national (district-level) COVID-19 statistics. We validated the CR-Index using machine learning methods by evaluating the out-of-sample predictive performance. Machine learning driven validation showed that the CR-Index can correctly predict districts with high incidents of COVID-19 cases and deaths more than 85% of the time. This proposed CR-Index is a simple, replicable, and easily interpretable tool that can help low-income countries prioritize resource mobilization to contain the disease spread and associated crisis management with global relevance and applicability. This index can also help to contain future pandemics (and epidemics) and manage their far-reaching adverse consequences.
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12
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Salim MM, Radwan AS, Hadad GM, Belal F, Elkhoudary MM. Green fluorometric strategy for simultaneous determination of the antihypertensive drug telmisartan (A tentative therapeutic for COVID-19) with Nebivolol in human plasma. Sci Rep 2023; 13:3576. [PMID: 36864220 PMCID: PMC9980868 DOI: 10.1038/s41598-023-30400-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 02/22/2023] [Indexed: 03/04/2023] Open
Abstract
Telmisartan (TEL) and Nebivolol (NEB) are frequently co-formulated in a single dosage form that is frequently prescribed for the treatment of hypertension, moreover, telmisartan is currently proposed to be used to treat COVID19-induced lung inflammation. Green rapid, simple, and sensitive synchronous spectrofluorimetric techniques for simultaneous estimation of TEL and NEB in their co-formulated pharmaceutical preparations and human plasma were developed and validated. Synchronous fluorescence intensity at 335 nm was used for TEL determination (Method I). For the mixture, the first derivative synchronous peak amplitudes (D1) at 296.3 and 320.5 nm were used for simultaneous estimation of NEB and TEL, respectively (Method II). The calibration plots were rectilinear over the concentration ranges of 30-550 ng/mL, and 50-800 ng/mL for NEB and TEL, respectively. The high sensitivity of the developed methods allowed for their analysis in human plasma samples. NEB`s Quantum yield was estimated by applying the single-point method. The greenness of the proposed approaches was evaluated using the Eco-scale, National Environmental Method Index (NEMI), and Green Analytical Procedure Index (GAPI) methods.
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Affiliation(s)
- Mohamed M Salim
- Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura, Egypt.
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Horus University- Egypt, New Damietta, Egypt.
| | - Aya Saad Radwan
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Horus University- Egypt, New Damietta, Egypt
- Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt
| | - Ghada M Hadad
- Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt
| | - Fathalla Belal
- Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura, Egypt
| | - Mahmoud M Elkhoudary
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Horus University- Egypt, New Damietta, Egypt
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13
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Noroozi-Ghaleini E, Shaibani MJ. Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks. Heliyon 2023; 9:e13672. [PMID: 36852029 PMCID: PMC9958458 DOI: 10.1016/j.heliyon.2023.e13672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 02/07/2023] [Accepted: 02/07/2023] [Indexed: 02/13/2023] Open
Abstract
Since 2019, the coronavirus outbreak has caused many catastrophic events all over the world. At the current time, the massive vaccination has been considered as the most efficient way to fight against the pandemic. This study schemes to explain and model COVID-19 cases by considering the vaccination rate. We utilized an amalgamation of neural network (NN) with two powerful optimization algorithms, i.e., firefly algorithm and artificial bee colony. For validating the models, we employed the COVID-19 datasets regarding the vaccination rate and the total confirmed cases for 51 states since the beginning of vaccination in the US. The numerical experiment indicated that by considering the vaccinated population, the accuracy of NN increases exponentially when compared with the same NN in the absence of the vaccinated population. During the next stage, the NN with vaccinated input data is elected for firefly and bee optimizing. Based upon the firefly optimizing, 93.75% of COVID-19 cases can be explained in all states. According to the bee optimizing, 92.3% of COVID-19 cases is explained since the massive vaccination. Overall, it can be concluded that the massive vaccination is the key predictor of COVID-19 cases on a grand scale.
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Affiliation(s)
- Ebrahim Noroozi-Ghaleini
- Mining Engineering Department, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran
- Corresponding author.
| | - Mohammad Javad Shaibani
- Department of Health Management and Economics, School of Public Health, Tehran University of MedicalSciences, Tehran, IranSciences, Tehran, Iran
- Corresponding author.
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14
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Kumar Y, Koul A, Kaur S, Hu YC. Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic. SN COMPUTER SCIENCE 2023; 4:91. [PMID: 36532634 PMCID: PMC9748400 DOI: 10.1007/s42979-022-01493-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/03/2022] [Indexed: 12/15/2022]
Abstract
In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat India
| | - Apeksha Koul
- Department of Computer Engineering, Punjabi University, Patiala, India
| | - Sukhpreet Kaur
- Department of Computer Science and Engineering, Chandigarh Engineering College, Landran, Mohali India
| | - Yu-Chen Hu
- Department of Computer Science and Information Management, Providence University, Taichung, Taiwan, ROC
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15
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Data analytics and knowledge management approach for COVID-19 prediction and control. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY : AN OFFICIAL JOURNAL OF BHARATI VIDYAPEETH'S INSTITUTE OF COMPUTER APPLICATIONS AND MANAGEMENT 2023; 15:937-954. [PMID: 35729979 PMCID: PMC9188422 DOI: 10.1007/s41870-022-00967-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 04/23/2022] [Indexed: 12/12/2022]
Abstract
The Coronavirus Disease (COVID-19) caused by SARS-CoV-2, continues to be a global threat. The major global concern among scientists and researchers is to develop innovative digital solutions for prediction and control of infection and to discover drugs for its cure. In this paper we developed a strategic technical solution for surveillance and control of COVID-19 in Delhi-National Capital Region (NCR). This work aims to elucidate the Delhi COVID-19 Data Management Framework, the backend mechanism of integrated Command and Control Center (iCCC) with plugged-in modules for various administrative, medical and field operations. Based on the time-series data extracted from iCCC repository, the forecasting of COVID-19 spread has been carried out for Delhi using the Auto-Regressive Integrated Moving Average (ARIMA) model as it can effectively predict the logistics requirements, active cases, positive patients, and death rate. The intelligence generated through this research has paved the way for the Government of National Capital Territory Delhi to strategize COVID-19 related policies formulation and implementation on real time basis. The outcome of this innovative work has led to the drastic reduction in COVID-19 positive cases and deaths in Delhi-NCR.
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16
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Liu B, Xie Y, Liu W, Jiang X, Ye Y, Song T, Chai J, Feng M, Yuan H. Nanophotonic reservoir computing for COVID-19 pandemic forecasting. NONLINEAR DYNAMICS 2022; 111:6895-6914. [PMID: 36588987 PMCID: PMC9792320 DOI: 10.1007/s11071-022-08190-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
The coronavirus disease 2019 (COVID-19) has spread worldwide in unprecedented speed, and diverse negative impacts have seriously endangered human society. Accurately forecasting the number of COVID-19 cases can help governments and public health organizations develop the right prevention strategies in advance to contain outbreaks. In this work, a long-term 6-month COVID-19 pandemic forecast in second half of 2021 and a short-term 30-day daily ahead COVID-19 forecast in December 2021 are successfully implemented via a novel nanophotonic reservoir computing based on silicon optomechanical oscillators with photonic crystal cavities, benefitting from its simpler learning algorithm, abundant nonlinear characteristics, and some unique advantages such as CMOS compatibility, fabrication cost, and monolithic integration. In essence, the nonlinear time series related to COVID-19 are mapped to the high-dimensional nonlinear space by the optical nonlinear properties of nanophotonic reservoir computing. The testing-dataset forecast results of new cases, new deaths, cumulative cases, and cumulative deaths for six countries demonstrate that the forecasted blue curves are awfully close to the real red curves with exceedingly small forecast errors. Moreover, the forecast results commendably reflect the variations of the actual case data, revealing the different epidemic transmission laws in developed and developing countries. More importantly, the daily ahead forecast results during December 2021 of four kinds of cases for six countries illustrate that the daily forecasted values are highly coincident with the real values, while the relevant forecast errors are tiny enough to verify the good forecasting competence of COVID-19 pandemic dominated by Omicron strain. Therefore, the implemented nanophotonic reservoir computing can provide some foreknowledge on prevention strategy and healthcare management for COVID-19 pandemic.
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Affiliation(s)
- Bocheng Liu
- School of Electronics and Information Engineering, Southwest University, Chongqing, 400715 China
| | - Yiyuan Xie
- School of Electronics and Information Engineering, Southwest University, Chongqing, 400715 China
- Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing, Chongqing, 400715 China
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, 400715 China
| | - Weichen Liu
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798 Singapore
| | - Xiao Jiang
- School of Electronics and Information Engineering, Southwest University, Chongqing, 400715 China
| | - Yichen Ye
- School of Electronics and Information Engineering, Southwest University, Chongqing, 400715 China
| | - Tingting Song
- School of Computer and Information Science, Chongqing Normal University, Chongqing, 401331 China
| | - Junxiong Chai
- School of Electronics and Information Engineering, Southwest University, Chongqing, 400715 China
| | - Manying Feng
- School of Electronics and Information Engineering, Southwest University, Chongqing, 400715 China
| | - Haodong Yuan
- School of Electronics and Information Engineering, Southwest University, Chongqing, 400715 China
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17
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Yaseen ASA. Impact of social determinants on COVID-19 infections: a comprehensive study from Saudi Arabia governorates. HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS 2022; 9:355. [PMID: 36249903 PMCID: PMC9540145 DOI: 10.1057/s41599-022-01208-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/19/2022] [Indexed: 06/16/2023]
Abstract
The last two years have been marked by the emergence of Coronavirus. The pandemic has spread in most countries, causing substantial changes all over the world. Many studies sought to analyze phenomena related to the pandemic from different perspectives. This study analyzes data from the governorates of the Kingdom of Saudi Arabia (the KSA), proposing a broad analysis that addresses three different research objectives. The first is to identify the main factors affecting the variations between KSA governorates in the cumulative number of COVID-19 infections. The study uses principal component regression. Results highlight the significant positive effects of the number of schools in each governorate, and classroom density within each school on the number of infections in the KSA. The second aim of this study is to use the number of COVID-19 infections, in addition to its significant predictors, to classify KSA governorates using the K-mean cluster method. Findings show that all KSA governorates can be grouped into two clusters. The first cluster includes 31 governorates that can be considered at greater risk of Covid infections as they have higher values in all the significant determinants of Covid infections. The last objective is to compare between traditional statistical methods and artificial intelligence techniques in predicting the future number of COVID-19 infections, with the aim of determining the method that provides the highest accuracy. Results also show that multilayer perceptron neural network outperforms others in forecasting the future number of COVID-19. Finally, the future number of infections for each cluster is predicted using multilayer perceptron neural network method.
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18
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Wolff J, Klimke A, Marschollek M, Kacprowski T. Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data. Sci Rep 2022; 12:15912. [PMID: 36151267 PMCID: PMC9508170 DOI: 10.1038/s41598-022-20190-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 09/09/2022] [Indexed: 12/03/2022] Open
Abstract
The COVID-19 pandemic has strong effects on most health care systems. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models. This would eventually allow to support timely resource allocation for optimal treatment of patients. We used admission data from 9 psychiatric hospitals in Germany between 2017 and 2020. We compared machine learning models with time series models in weekly, monthly and yearly forecasting before and during the COVID-19 pandemic. A total of 90,686 admissions were analysed. The models explained up to 90% of variance in hospital admissions in 2019 and 75% in 2020 with the effects of the COVID-19 pandemic. The best models substantially outperformed a one-step seasonal naïve forecast (seasonal mean absolute scaled error (sMASE) 2019: 0.59, 2020: 0.76). The best model in 2019 was a machine learning model (elastic net, mean absolute error (MAE): 7.25). The best model in 2020 was a time series model (exponential smoothing state space model with Box-Cox transformation, ARMA errors and trend and seasonal components, MAE: 10.44). Models forecasting admissions one week in advance did not perform better than monthly and yearly models in 2019 but they did in 2020. The most important features for the machine learning models were calendrical variables. Model performance did not vary much between different modelling approaches before the COVID-19 pandemic and established forecasts were substantially better than one-step seasonal naïve forecasts. However, weekly time series models adjusted quicker to the COVID-19 related shock effects. In practice, multiple individual forecast horizons could be used simultaneously, such as a yearly model to achieve early forecasts for a long planning period and weekly models to adjust quicker to sudden changes.
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Affiliation(s)
- J Wolff
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, Carl-Neuberg-Straße 1, 30625, Hannover, Germany. .,Marienstift Hospital, Helmstedter Straße 35, 38102, Braunschweig, Germany.
| | - A Klimke
- Vitos Hochtaunus, Friedrichsdorf, Emil-Sioli-Weg 1-3, 61381, Friedrichsdorf, Germany.,Heinrich-Heine-University Duesseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - M Marschollek
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, Carl-Neuberg-Straße 1, 30625, Hannover, Germany
| | - T Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, TU Braunschweig, Rebenring 56, 38106, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Rebenring 56, 38106, Braunschweig, Germany
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19
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Abu-Abdoun DI, Al-Shihabi S. Weather Conditions and COVID-19 Cases: Insights from the GCC Countries. INTELLIGENT SYSTEMS WITH APPLICATIONS 2022. [PMCID: PMC9213049 DOI: 10.1016/j.iswa.2022.200093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The prediction of new COVID-19 cases is crucial for decision makers in many countries. Researchers are continually proposing new models to forecast the future tendencies of this pandemic, among which long short-term memory (LSTM) artificial neural networks have exhibited relative superiority compared to other forecasting techniques. Moreover, the correlation between the spread of COVID-19 and exogenous factors, specifically weather features, has been explored to improve forecasting models. However, contradictory results have been reported regarding the incorporation of weather features into COVID-19 forecasting models. Therefore, this study compares uni-variate with bi- and multi-variate LSTM forecasting models for predicting COVID-19 cases, among which the latter models consider weather features. LSTM models were used to forecast COVID-19 cases in the six Gulf Cooperation Council countries. The root mean square error (RMSE) and coefficient of determination (R2) were employed to measure the accuracy of the LSTM forecasting models. Despite similar weather conditions, the weather features that exhibited the strongest correlation with COVID-19 cases differed among the six countries. Moreover, according to the statistical comparisons that were conducted, the improvements gained by including weather features were insignificant in terms of the RMSE values and marginally significant in terms of the R2 values. Consequently, it is concluded that the uni-variate LSTM models were as good as the best bi- and multi-variate LSTM models; therefore, weather features need not be included. Furthermore, we could not identify a single weather feature that can consistently improve the forecasting accuracy.
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20
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Jin W, Dong S, Yu C, Luo Q. A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning. Comput Biol Med 2022; 146:105560. [PMID: 35551008 PMCID: PMC9042415 DOI: 10.1016/j.compbiomed.2022.105560] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 03/18/2022] [Accepted: 04/06/2022] [Indexed: 01/31/2023]
Abstract
The COVID-19 outbreak poses a huge challenge to international public health. Reliable forecast of the number of cases is of great significance to the planning of health resources and the investigation and evaluation of the epidemic situation. The data-driven machine learning models can adapt to complex changes in the epidemic situation without relying on correct physical dynamics modeling, which are sensitive and accurate in predicting the development of the epidemic. In this paper, an ensemble hybrid model based on Temporal Convolutional Networks (TCN), Gated Recurrent Unit (GRU), Deep Belief Networks (DBN), Q-learning, and Support Vector Machine (SVM) models, namely TCN-GRU-DBN-Q-SVM model, is proposed to achieve the forecasting of COVID-19 infections. Three widely-used predictors, TCN, GRU, and DBN are used as elements of the hybrid model ensembled by the weights provided by reinforcement learning method. Furthermore, an error predictor built by SVM, is trained with validation set, and the final prediction result could be obtained by combining the TCN-GRU-DBN-Q model with the SVM error predictor. In order to investigate the forecasting performance of the proposed hybrid model, several comparison models (TCN-GRU-DBN-Q, LSTM, N-BEATS, ANFIS, VMD-BP, WT-RVFL, and ARIMA models) are selected. The experimental results show that: (1) the prediction effect of the TCN-GRU-DBN-Q-SVM model on COVID-19 infection is satisfactory, which has been verified in three national infection data from the UK, India, and the US, and the proposed model has good generalization ability; (2) in the proposed hybrid model, SVM can efficiently predict the possible error of the predicted series given by TCN-GRU-DBN-Q components; (3) the integrated weights based on Q-learning can be adaptively adjusted according to the characteristics of the data in the forecasting tasks in different countries and multiple situations, which ensures the accuracy, robustness and generalization of the proposed model.
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Affiliation(s)
- Weiqiu Jin
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, PR China,School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, PR China
| | - Shuqing Dong
- School of Traffic and Transportation Engineering, Central South University, Hunan, 410075, PR China
| | - Chengqing Yu
- School of Traffic and Transportation Engineering, Central South University, Hunan, 410075, PR China
| | - Qingquan Luo
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, PR China,School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, PR China,Corresponding author. Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, PR China
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21
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Luo Z, Jia X, Bao J, Song Z, Zhu H, Liu M, Yang Y, Shi X. A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105910. [PMID: 35627447 PMCID: PMC9141474 DOI: 10.3390/ijerph19105910] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/07/2022] [Accepted: 05/10/2022] [Indexed: 01/27/2023]
Abstract
Acquired immune deficiency syndrome (AIDS) is a serious public health problem. This study aims to establish a combined model of seasonal autoregressive integrated moving average (SARIMA) and Prophet models based on an L1-norm to predict the incidence of AIDS in Henan province, China. The monthly incidences of AIDS in Henan province from 2012 to 2020 were obtained from the Health Commission of Henan Province. A SARIMA model, a Prophet model, and two combined models were adopted to fit the monthly incidence of AIDS using the data from January 2012 to December 2019. The data from January 2020 to December 2020 was used to verify. The mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the prediction effect among the models. The results showed that the monthly incidence fluctuated from 0.05 to 0.50 per 100,000 individuals, and the monthly incidence of AIDS had a certain periodicity in Henan province. In addition, the prediction effect of the Prophet model was better than SARIMA model, the combined model was better than the single models, and the combined model based on the L1-norm had the best effect values (MSE = 0.0056, MAE = 0.0553, MAPE = 43.5337). This indicated that, compared with the L2-norm, the L1-norm improved the prediction accuracy of the combined model. The combined model of SARIMA and Prophet based on the L1-norm is a suitable method to predict the incidence of AIDS in Henan. Our findings can provide theoretical evidence for the government to formulate policies regarding AIDS prevention.
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22
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Fabiano B, Hailwood M, Thomas P. Safety, environmental and risk management related to Covid-19. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2022; 160:397-399. [PMID: 35194340 PMCID: PMC8849899 DOI: 10.1016/j.psep.2022.02.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Affiliation(s)
- Bruno Fabiano
- University of Genoa, DICCA - Department of Civil, Chemical and Environmental Engineering, Genoa, Italy
| | - Mark Hailwood
- LUBW Landesanstalt für Umwelt Baden-Württemberg, Karlsruhe, Germany
| | - Philip Thomas
- University of Bristol, Faculty of Engineering, Bristol, United Kingdom
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23
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Moslehpour M, Al-Fadly A, Ehsanullah S, Chong KW, Xuyen NTM, Tan LP. Assessing Financial Risk Spillover and Panic Impact of Covid-19 on European and Vietnam Stock market. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:28226-28240. [PMID: 34993822 PMCID: PMC8736318 DOI: 10.1007/s11356-021-18170-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 12/13/2021] [Indexed: 05/23/2023]
Abstract
This study examined the influence of tail risks on global financial markets, which aids in better understanding of the emergence of COVID-19. This study looks at the global and Vietnamese stock markets impacted by the COVID-19 pandemic to identify systemic emergencies. Risk dependent value (CoVaR) and Delta link VaR are two important tail-related risk indicators used in Conditional Bivariate Dynamic Correlation (DCC) (CoVaR). The empirical findings demonstrate that when COVID-19's worldwide spread widens, the volatility transmission of systemic risks across the global stock market and multiple exchanges shifts and becomes more relevant over time. At the time of COVID-19, the world industrial market was larger than the Vietnamese stock market, and the Vietnamese stock market posed a lesser danger to the global market. A closer examination of the link between the Vietnam value-at-risk (VaR) range index sample and the world stock index indicates a significant degree of downside risk integration in key monetary systems, particularly during the COVID-19 era. Our study findings may help regulators, politicians, and portfolio risk managers in Vietnam and worldwide during the unique moment of uncertainty created by the COVID-19 epidemic.
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Affiliation(s)
- Massoud Moslehpour
- Department of Business Administration, Asia University, 500, Lioufeng Rd., Wufeng, Taichung, 41354 Taiwan
- Department of Management, California State University, San Bernardino 5500, University Parkway, San Bernardino, CA 92407 USA
| | - Ahmad Al-Fadly
- Gulf University for Science & Technology, Mubarak Al-Abdullah, Kuwait
| | | | - Kwong Wing Chong
- School of Professional Studies, Taylor’s College, Taylor’s Lakeside Campus, No. 1 Jalan Taylor’s, 47500 Subang Jaya, Selangor Malaysia
| | - Nguyen Thi My Xuyen
- Faculty of Business Administration, Van Lang University, 69/68 Dang Thuy Tram, Ward 13, Binh Thanh Dist., Ho Chi Minh City, Vietnam
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24
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Mokhber A, Aggarwal S, García‐Triñanes P. Application of layers of protection analysis to prevent coronavirus infection. PROCESS SAFETY PROGRESS 2022. [PMCID: PMC9111029 DOI: 10.1002/prs.12362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Layers of protection analysis (LOPA) methodology is applied to an encounter with the SARS‐COV‐2 infection as an initiating event, and subsequently, independent protection layers (IPLs) (namely health safeguarding protocols), such as social distancing, ventilation, hand hygiene, face masks, and vaccinations. LOPA is applied considering numerical quantification of the COVID fatality index in order to manage the transmission risk to a tolerable level, namely the fatality risk due to seasonal flu. This measurement tool quantifies the ratio of the annual death rate due to the SARS‐COV‐2 infection to the annual death rate of the common flu, and it is applied to a chemical plant. The lower this quantified value is, the more the COVID‐19 infection death rate approaches that of the common flu. Thus, any improvement in safeguarding protocols should reduce this index. The input data is based on public domain COVID‐19 infection statistical data and websites accessible in the United Kingdom. The COVID‐19 transmission rate is statistically analyzed with random number sampling to simulate the random pattern of the virus' person‐to‐person infection in the community. The success of the COVID‐19 protection protocols is probabilistic and depends on the public's compliance, which is modeled by observational surveys.
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Affiliation(s)
- Ali Mokhber
- Materials and Chemical Engineering Group, School of Engineering University of Greenwich Medway UK
| | - Shivani Aggarwal
- Materials and Chemical Engineering Group, School of Engineering University of Greenwich Medway UK
| | - Pablo García‐Triñanes
- Materials and Chemical Engineering Group, School of Engineering University of Greenwich Medway UK
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25
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Determining an effective short term COVID-19 prediction model in ASEAN countries. Sci Rep 2022; 12:5083. [PMID: 35332192 PMCID: PMC8943510 DOI: 10.1038/s41598-022-08486-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 03/03/2022] [Indexed: 12/04/2022] Open
Abstract
The challenge of accurately short-term forecasting demand is due to model selection and the nature of data trends. In this study, the prediction model was determined based on data patterns (trend data without seasonality) and the accuracy of prediction measurement. The cumulative number of COVID-19 affected people in some ASEAN countries had been collected from the Worldometers database. Three models [Holt’s method, Wright’s modified Holt’s method, and unreplicated linear functional relationship model (ULFR)] had been utilized to identify an efficient model for short-time prediction. Moreover, different smoothing parameters had been tested to find the best combination of the smoothing parameter. Nevertheless, using the day-to-day reported cumulative case data and 3-days and 7-days in advance forecasts of cumulative data. As there was no missing data, Holt’s method and Wright’s modified Holt’s method showed the same result. The text-only result corresponds to the consequences of the models discussed here, where the smoothing parameters (SP) were roughly estimated as a function of forecasting the number of affected people due to COVID-19. Additionally, the different combinations of SP showed diverse, accurate prediction results depending on data volume. Only 1-day forecasting illustrated the most efficient prediction days (1 day, 3 days, 7 days), which was validated by the Nash–Sutcliffe efficiency (NSE) model. The study also validated that ULFR was an efficient forecasting model for the efficient model identifying. Moreover, as a substitute for the traditional R-squared, the study applied NSE and R-squared (ULFR) for model selection. Finally, the result depicted that the prediction ability of ULFR was superior to Holt’s when it is compared to the actual data.
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26
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Irfan M, Shahid AL, Ahmad M, Iqbal W, Elavarasan RM, Ren S, Hussain A. Assessment of public intention to get vaccination against COVID-19: Evidence from a developing country. J Eval Clin Pract 2022; 28:63-73. [PMID: 34427007 PMCID: PMC8657341 DOI: 10.1111/jep.13611] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/26/2021] [Accepted: 08/03/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVES Widespread acceptance of the COVID-19 vaccine will be the next important step in fighting the novel coronavirus disease. Though the Pakistani government has successfully implemented robust policies to overcome the COVID-19 pandemic; however, studies assessing public intention to get COVID-19 vaccination (IGCV) are limited. The aim of this study is to deal with this literature gap and has also expanded the conceptual framework of planned behaviour theory. We have introduced three new considerations (risk perceptions of the pandemic, perceived benefits of the vaccine, and unavailability of vaccine) to have a better understanding of the influencing factors that encourage or discourage public IGCV. METHODS Results are based on a sample collected from 754 households using an inclusive questionnaire survey. Hypotheses are tested by utilizing the structural equation modelling approach. RESULTS The results disclose that the intention factors, that is, attitude, risk perceptions of the pandemic, and perceived benefits of the vaccine, impart positive effects on public IGCV. In contrast, the cost of the vaccine and the unavailability of the vaccine have negative effects. Notably, environmental concern has an insignificant effect. CONCLUSIONS Research findings emphasize the importance of publicizing the devastating impacts of COVID-19 on society and the environment, ensuring vaccination availability at an accessible price while simultaneously improving public healthcare practices.
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Affiliation(s)
- Muhammad Irfan
- School of Management and Economics, Beijing Institute of Technology, Beijing, China.,Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China
| | - Abdul Latif Shahid
- Pediatric Orthopedic Surgery Department, The Children Hospital and Institute of Child Health, Lahore, Pakistan
| | - Munir Ahmad
- School of Economics, Zhejiang University, Hangzhou, China
| | - Wasim Iqbal
- Department of Management Science, College of Management, Shenzhen University, Shenzhen, China
| | | | - Siyu Ren
- School of Economics, Nankai University, Tianjin, China
| | - Abid Hussain
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China
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27
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Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06747-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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28
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Shafiekhani S, Namdar P, Rafiei S. A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit. Digit Health 2022; 8:20552076221085057. [PMID: 35355809 PMCID: PMC8961204 DOI: 10.1177/20552076221085057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 01/13/2022] [Accepted: 02/16/2022] [Indexed: 12/23/2022] Open
Abstract
Background Centers for Disease Control and Prevention data showed that about 40% of coronavirus disease 2019 (COVID-19) patients had been suffering from at least one underlying medical condition were hospitalized; in which nearly 33% of them needed to be admitted to the intensive care unit (ICU) to receive specialized medical services. Our study aimed to find a proper machine learning algorithm that can predict confirmed COVID-19 hospital admissions with high accuracy. Methods We obtained data on daily COVID-19 cases in regular medical inpatient units, emergency department, and ICU in the time window between 21 July 2020 and 21 November 2021. Data for the first 183 days (training data set) were used for long short-term memory (LSTM) network, adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and decision tree model training, whilst the remaining data for the last 60 days (test data set) were used for model validation. To predict the number of ICU and non-ICU patients, we used these models. Finally, a user-friendly graphical user interface unit was designed to load any time series data (here the trend of population of COVID-19 patients) and train LSTM, ANFIS, SVR or tree models for the prediction of COVID-19 cases for one week ahead. Results All models predicted the dynamics of COVID-19 cases in ICU and non- wards. The values of root-mean-square error and R2 as model assessment metrics showed that ANFIS model had better predictive power among all models. Conclusion Artificial intelligence-based forecasting models such as ANFIS system or deep learning approach based on LSTM or regression models including SVR or tree regression play a key role in forecasting the required number of beds or other types of medical facilities during the coronavirus pandemic. Thus, the designed graphical user interface of the present study can be used for optimum management of resources by health care systems amid COVID-19 pandemic.
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Affiliation(s)
- Sajad Shafiekhani
- Department of Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics, Tehran, Iran
- Students’ Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Peyman Namdar
- Social Determinants of Health Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran
| | - Sima Rafiei
- Department of Healthcare Management, School of Health, Qazvin University of Medical Sciences, Qazvin, Iran
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29
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El-Shorbagy M, El-Refaey AM. COVID-19: Mathematical growth vs. precautionary measures in China, KSA, and the USA. INFORMATICS IN MEDICINE UNLOCKED 2022; 28:100834. [PMID: 34977332 PMCID: PMC8713421 DOI: 10.1016/j.imu.2021.100834] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 12/21/2021] [Accepted: 12/21/2021] [Indexed: 02/08/2023] Open
Abstract
This paper aims to study the relation between precautionary measures that were taken by countries to prevent the spread of COVID-19 and its impact on its mathematical growth. In this paper, we study the development and growth of the epidemic during the first fifty days since its appearance in three countries: China, the Kingdom of Saudi Arabia (KSA), and the United States of America (USA). An optimization process is used to determine the parameters of the closest model that simulates the data during the specified period by using one of the evolutionary computation techniques, the grasshopper optimization algorithm (GOA). The study reveals that the strict precautionary measures of applying isolation and quarantine, preventing all gatherings, and a total curfew are the only way to prevent the spread of the epidemic exponentially as China did. Also, without any measures to slow its growth, COVID-19 will continue to spread steadily for months.
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Affiliation(s)
- M.A. El-Shorbagy
- Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia,Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, Egypt,Corresponding author. Department of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Adel M. El-Refaey
- Basic and Applied Science Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Smart Village Campus, Egypt
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30
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Dong Y, Zhou S, Xing L, Chen Y, Ren Z, Dong Y, Zhang X. Deep learning methods may not outperform other machine learning methods on analyzing genomic studies. Front Genet 2022; 13:992070. [PMID: 36212148 PMCID: PMC9537734 DOI: 10.3389/fgene.2022.992070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/04/2022] [Indexed: 12/03/2022] Open
Abstract
Deep Learning (DL) has been broadly applied to solve big data problems in biomedical fields, which is most successful in image processing. Recently, many DL methods have been applied to analyze genomic studies. However, genomic data usually has too small a sample size to fit a complex network. They do not have common structural patterns like images to utilize pre-trained networks or take advantage of convolution layers. The concern of overusing DL methods motivates us to evaluate DL methods' performance versus popular non-deep Machine Learning (ML) methods for analyzing genomic data with a wide range of sample sizes. In this paper, we conduct a benchmark study using the UK Biobank data and its many random subsets with different sample sizes. The original UK Biobank data has about 500k participants. Each patient has comprehensive patient characteristics, disease histories, and genomic information, i.e., the genotypes of millions of Single-Nucleotide Polymorphism (SNPs). We are interested in predicting the risk of three lung diseases: asthma, COPD, and lung cancer. There are 205,238 participants have recorded disease outcomes for these three diseases. Five prediction models are investigated in this benchmark study, including three non-deep machine learning methods (Elastic Net, XGBoost, and SVM) and two deep learning methods (DNN and LSTM). Besides the most popular performance metrics, such as the F1-score, we promote the hit curve, a visual tool to describe the performance of predicting rare events. We discovered that DL methods frequently fail to outperform non-deep ML in analyzing genomic data, even in large datasets with over 200k samples. The experiment results suggest not overusing DL methods in genomic studies, even with biobank-level sample sizes. The performance differences between DL and non-deep ML decrease as the sample size of data increases. This suggests when the sample size of data is significant, further increasing sample sizes leads to more performance gain in DL methods. Hence, DL methods could be better if we analyze genomic data bigger than this study.
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Affiliation(s)
- Yao Dong
- School of Artifcial Intelligence, Hebei University of Technology, Tianjin, China.,Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.,Hebei Province Key Laboratory of Big Data Computing, Tianjin, China
| | - Shaoze Zhou
- Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada
| | - Li Xing
- Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, Saskatoon
| | - Yumeng Chen
- School of Artifcial Intelligence, Hebei University of Technology, Tianjin, China.,Hebei Province Key Laboratory of Big Data Computing, Tianjin, China
| | - Ziyu Ren
- School of Artifcial Intelligence, Hebei University of Technology, Tianjin, China.,Hebei Province Key Laboratory of Big Data Computing, Tianjin, China
| | - Yongfeng Dong
- School of Artifcial Intelligence, Hebei University of Technology, Tianjin, China.,Hebei Province Key Laboratory of Big Data Computing, Tianjin, China
| | - Xuekui Zhang
- Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada
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31
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Saif S, Das P, Biswas S. A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast. JOURNAL OF THE INSTITUTION OF ENGINEERS (INDIA): SERIES B 2021. [PMCID: PMC7814866 DOI: 10.1007/s40031-021-00538-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Sohail Saif
- Department of Computer Science & Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, WB India
| | - Priya Das
- Department of Computer Science & Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, WB India
| | - Suparna Biswas
- Department of Computer Science & Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, WB India
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32
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Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview. Healthcare (Basel) 2021; 9:healthcare9121614. [PMID: 34946340 PMCID: PMC8700845 DOI: 10.3390/healthcare9121614] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/12/2021] [Accepted: 11/19/2021] [Indexed: 12/23/2022] Open
Abstract
Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.
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33
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Ayyildiz E, Erdogan M, Taskin A. Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain. Comput Biol Med 2021; 139:105029. [PMID: 34794082 PMCID: PMC8590479 DOI: 10.1016/j.compbiomed.2021.105029] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 12/23/2022]
Abstract
This study introduces a forecasting model to help design an effective blood supply chain mechanism for tackling the COVID-19 pandemic. In doing so, first, the number of people recovered from COVID-19 is forecasted using the Artificial Neural Networks (ANNs) to determine potential donors for convalescent (immune) plasma (CIP) treatment of COVID-19. This is performed explicitly to show the applicability of ANNs in forecasting the daily number of patients recovered from COVID-19. Second, the ANNs-based approach is further applied to the data from Italy to confirm its robustness in other geographical contexts. Finally, to evaluate its forecasting accuracy, the proposed Multi-Layer Perceptron (MLP) approach is compared with other traditional models, including Autoregressive Integrated Moving Average (ARIMA), Long Short-term Memory (LSTM), and Nonlinear Autoregressive Network with Exogenous Inputs (NARX). Compared to the ARIMA, LSTM, and NARX, the MLP-based model is found to perform better in forecasting the number of people recovered from COVID-19. Overall, the findings suggest that the proposed model is robust and can be widely applied in other parts of the world in forecasting the patients recovered from COVID-19.
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Affiliation(s)
- Ertugrul Ayyildiz
- Department of Industrial Engineering, Karadeniz Technical University, Ortahisar, 61080, Trabzon, Turkey; Department of Industrial Engineering, Yildiz Technical University, Beşiktaş, 34349, İstanbul, Turkey.
| | - Melike Erdogan
- Department of Industrial Engineering, Duzce University, Konuralp, 81620, Duzce, Turkey
| | - Alev Taskin
- Department of Industrial Engineering, Yildiz Technical University, Beşiktaş, 34349, İstanbul, Turkey
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34
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Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm. ENTROPY 2021; 23:e23111383. [PMID: 34828081 PMCID: PMC8624090 DOI: 10.3390/e23111383] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/12/2021] [Accepted: 10/13/2021] [Indexed: 12/23/2022]
Abstract
Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics.
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35
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A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting. FRACTAL AND FRACTIONAL 2021. [DOI: 10.3390/fractalfract5040175] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
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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36
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Abd Elaziz M, Essa F, Elsheikh AH. Utilization of ensemble random vector functional link network for freshwater prediction of active solar stills with nanoparticles. SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021; 47:101405. [DOI: 10.1016/j.seta.2021.101405] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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37
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Marzouk M, Elshaboury N, Abdel-Latif A, Azab S. Deep learning model for forecasting COVID-19 outbreak in Egypt. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2021; 153:363-375. [PMID: 34334966 PMCID: PMC8305306 DOI: 10.1016/j.psep.2021.07.034] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 07/16/2021] [Accepted: 07/22/2021] [Indexed: 05/21/2023]
Abstract
The World Health Organization has declared COVID-19 as a global pandemic in early 2020. A comprehensive understanding of the epidemiological characteristics of this virus is crucial to limit its spreading. Therefore, this research applies artificial intelligence-based models to predict the prevalence of the COVID-19 outbreak in Egypt. These models are long short-term memory network (LSTM), convolutional neural network, and multilayer perceptron neural network. They are trained and validated using the dataset records from 14 February 2020 to 15 August 2020. The results of the models are evaluated using the determination coefficient and root mean square error. The LSTM model exhibits the best performance in forecasting the cumulative infections for one week and one month ahead. Finally, the LSTM model with the optimal parameter values is applied to forecast the spread of this epidemic for one month ahead using the data from 14 February 2020 to 30 June 2021. The total size of infections, recoveries, and deaths is estimated to be 285,939, 234,747, and 17,251 cases on 31 July 2021. This study could assist the decision-makers in developing and monitoring policies to confront this disease.
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Affiliation(s)
- Mohamed Marzouk
- Structural Engineering Department, Faculty of Engineering, Cairo University, Egypt
| | - Nehal Elshaboury
- Construction and Project Management Research Institute, Housing and Building National Research Center, Giza, Egypt
| | - Amr Abdel-Latif
- Project Management Division, Alsafa Real Estate Development Inc., Cairo, Egypt
| | - Shimaa Azab
- Environmental Planning and Development Center, Institute of National Planning, (INP), Cairo, Egypt
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38
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Abstract
The highly infectious COVID-19 critically affected the world that has stuck millions of citizens in their homes to avoid possible spreading of the disease. Researchers in different fields are continually working to develop vaccines and prevention strategies. However, an accurate forecast of the outbreak can help control the pandemic until a vaccine is available. Several machine learning and deep learning-based approaches are available to forecast the confirmed cases, but they lack the optimized temporal component and nonlinearity. To enhance the current forecasting frameworks' capability, we proposed optimized long short-term memory networks (LSTM) to forecast COVID-19 cases and reduce mean absolute error. For the optimization of LSTM, we applied bat algorithm. Furthermore, to tackle the premature convergence and local minima problem of BA, we proposed an enhanced variant of BA. The proposed version utilized Gaussian adaptive inertia weight to control the individual velocity in the entire swarm. In addition, we substitute random walk with the Gaussian walk to observe the local search mechanism. The proposed LSTM examines the personal best solution with the swarm's local best and preserves the optimal solution by combining the Gaussian walk. To evaluate the optimized LSTM, we compared it with the non-optimal version of LSTM, recurrent neural network, gated recurrent units, and other recent state-of-the-art algorithms. The experimental results prove the superiority of the optimized LSTM over other recent algorithms by obtaining 99.52 % accuracy.
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39
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Kolozsvári LR, Bérczes T, Hajdu A, Gesztelyi R, Tiba A, Varga I, Al-Tammemi AB, Szőllősi GJ, Harsányi S, Garbóczy S, Zsuga J. Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves. INFORMATICS IN MEDICINE UNLOCKED 2021; 25:100691. [PMID: 34395821 PMCID: PMC8349399 DOI: 10.1016/j.imu.2021.100691] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 07/21/2021] [Accepted: 08/01/2021] [Indexed: 12/15/2022] Open
Abstract
Objectives The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data. Methods We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data. Results We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low. Conclusion Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.
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Affiliation(s)
- László Róbert Kolozsvári
- Department of Family and Occupational Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.,Doctoral School of Health Sciences, University of Debrecen, Debrecen, Hungary
| | - Tamás Bérczes
- Faculty of Informatics, University of Debrecen, Debrecen, Hungary
| | - András Hajdu
- Faculty of Informatics, University of Debrecen, Debrecen, Hungary
| | - Rudolf Gesztelyi
- Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Attila Tiba
- Faculty of Informatics, University of Debrecen, Debrecen, Hungary
| | - Imre Varga
- Faculty of Informatics, University of Debrecen, Debrecen, Hungary
| | - Ala'a B Al-Tammemi
- Department of Family and Occupational Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.,Doctoral School of Health Sciences, University of Debrecen, Debrecen, Hungary
| | - Gergő József Szőllősi
- Department of Family and Occupational Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Szilvia Harsányi
- Doctoral School of Health Sciences, University of Debrecen, Debrecen, Hungary.,Department of Health Systems Management and Quality Management in Health Care, Faculty of Public Health, University of Debrecen, Debrecen, Hungary
| | - Szabolcs Garbóczy
- Doctoral School of Health Sciences, University of Debrecen, Debrecen, Hungary.,Department of Psychiatry, Kenézy Hospital, University of Debrecen, Debrecen, Hungary
| | - Judit Zsuga
- Department of Health Systems Management and Quality Management in Health Care, Faculty of Public Health, University of Debrecen, Debrecen, Hungary
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40
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Kolozsvári LR, Bérczes T, Hajdu A, Gesztelyi R, Tiba A, Varga I, Al-Tammemi AB, Szőllősi GJ, Harsányi S, Garbóczy S, Zsuga J. Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves. INFORMATICS IN MEDICINE UNLOCKED 2021; 25:100691. [PMID: 34395821 DOI: 10.1101/2020.04.17.20069666] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 07/21/2021] [Accepted: 08/01/2021] [Indexed: 05/27/2023] Open
Abstract
OBJECTIVES The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data. METHODS We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data. RESULTS We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low. CONCLUSION Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.
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Affiliation(s)
- László Róbert Kolozsvári
- Department of Family and Occupational Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Doctoral School of Health Sciences, University of Debrecen, Debrecen, Hungary
| | - Tamás Bérczes
- Faculty of Informatics, University of Debrecen, Debrecen, Hungary
| | - András Hajdu
- Faculty of Informatics, University of Debrecen, Debrecen, Hungary
| | - Rudolf Gesztelyi
- Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Attila Tiba
- Faculty of Informatics, University of Debrecen, Debrecen, Hungary
| | - Imre Varga
- Faculty of Informatics, University of Debrecen, Debrecen, Hungary
| | - Ala'a B Al-Tammemi
- Department of Family and Occupational Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Doctoral School of Health Sciences, University of Debrecen, Debrecen, Hungary
| | - Gergő József Szőllősi
- Department of Family and Occupational Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Szilvia Harsányi
- Doctoral School of Health Sciences, University of Debrecen, Debrecen, Hungary
- Department of Health Systems Management and Quality Management in Health Care, Faculty of Public Health, University of Debrecen, Debrecen, Hungary
| | - Szabolcs Garbóczy
- Doctoral School of Health Sciences, University of Debrecen, Debrecen, Hungary
- Department of Psychiatry, Kenézy Hospital, University of Debrecen, Debrecen, Hungary
| | - Judit Zsuga
- Department of Health Systems Management and Quality Management in Health Care, Faculty of Public Health, University of Debrecen, Debrecen, Hungary
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Coronavirus Disease in Children: A Single-Center Study from Western Saudi Arabia. Int J Pediatr 2021; 2021:9918056. [PMID: 34394360 PMCID: PMC8360710 DOI: 10.1155/2021/9918056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 07/14/2021] [Accepted: 07/18/2021] [Indexed: 01/10/2023] Open
Abstract
Introduction Local data in Saudi Arabia regarding pediatric SARS-CoV-2 infection is limited. This study is aimed at adding insight regarding the effect of the novel coronavirus on pediatric patients by studying the presentation, laboratory parameters, and disposition of SARS-CoV-2-infected pediatric patients in one center in Jeddah, Saudi Arabia. Methodology. A retrospective study was conducted at the International Medical Center (IMC) in Jeddah, Saudi Arabia, to assess features of pediatric patients admitted with COVID-19 from April 2020 to September 2020. Results A total of 43 patients were found to meet the study inclusion criteria. The most common presenting symptom was fever (53.5%) in study participants followed by complaints of cough, runny nose, and shortness of breath (37.2%). Lymphocytopenia was evident among 60% of those studied. Elevated C-Reactive Protein was remarkable in 24.9%. More than half of those (53.5%) studied required only supportive treatment. Conclusion COVID-19 disease for the most part is mild in children with a varying clinical picture and nonspecific laboratory parameters. Further, large-scale national-based studies are needed to help in the early identification of pediatric cases at risk of complication due to COVID-19 infection hence providing proper and timely management, identifying population-specific disease pattern and perhaps targeted immunization.
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Shoaib M, Salahudin H, Hammad M, Ahmad S, Khan AA, Khan MM, Baig MAI, Ahmad F, Ullah MK. Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases. SN COMPUTER SCIENCE 2021; 2:372. [PMID: 34258586 PMCID: PMC8267227 DOI: 10.1007/s42979-021-00764-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 07/02/2021] [Indexed: 11/10/2022]
Abstract
An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2, RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead.
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Affiliation(s)
- Muhammad Shoaib
- Agricultural Engineering Department, Bahauddin Zakariya University, Multan, Pakistan
| | - Hamza Salahudin
- Agricultural Engineering Department, Bahauddin Zakariya University, Multan, Pakistan
| | - Muhammad Hammad
- Department of Agricultural Engineering, Bahauddin Zakariya University, Multan, Pakistan
| | - Shakil Ahmad
- NUST Institute of Civil Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Alamgir Akhtar Khan
- Department of Agricultural Engineering, MNS University of Agriculture, Multan, Pakistan
| | - Mudasser Muneer Khan
- Department of Civil Engineering, Bahauddin Zakariya University, Multan, Pakistan
| | | | - Fiaz Ahmad
- Department of Agricultural Engineering, Bahauddin Zakariya University, Multan, Pakistan
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Ding W, Nayak J, Swapnarekha H, Abraham A, Naik B, Pelusi D. Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data. Neurocomputing 2021; 457:40-66. [PMID: 34149184 PMCID: PMC8206574 DOI: 10.1016/j.neucom.2021.06.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 06/02/2021] [Accepted: 06/11/2021] [Indexed: 12/11/2022]
Abstract
The unprecedented surge of a novel coronavirus in the month of December 2019, named as COVID-19 by the World Health organization has caused a serious impact on the health and socioeconomic activities of the public all over the world. Since its origin, the number of infected and deceased cases has been growing exponentially in almost all the affected countries of the world. The rapid spread of the novel coronavirus across the world results in the scarcity of medical resources and overburdened hospitals. As a result, the researchers and technocrats are continuously working across the world for the inculcation of efficient strategies which may assist the government and healthcare system in controlling and managing the spread of the COVID-19 pandemic. Therefore, this study provides an extensive review of the ongoing strategies such as diagnosis, prediction, drug and vaccine development and preventive measures used in combating the COVID-19 along with technologies used and limitations. Moreover, this review also provides a comparative analysis of the distinct type of data, emerging technologies, approaches used in diagnosis and prediction of COVID-19, statistics of contact tracing apps, vaccine production platforms used in the COVID-19 pandemic. Finally, the study highlights some challenges and pitfalls observed in the systematic review which may assist the researchers to develop more efficient strategies used in controlling and managing the spread of COVID-19.
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Affiliation(s)
- Weiping Ding
- School of Information Science and Technology, Nantong University, China
| | - Janmenjoy Nayak
- Aditya Institute of Technology and Management (AITAM), India
| | - H Swapnarekha
- Aditya Institute of Technology and Management (AITAM), India
- Veer Surendra Sai University of Technology, India
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Alharbi F. The Use of Digital Healthcare Platforms During the COVID-19 Pandemic: the Consumer Perspective. Acta Inform Med 2021; 29:51-58. [PMID: 34012214 PMCID: PMC8116074 DOI: 10.5455/aim.2021.29.51-58] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 03/17/2021] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND The spending on digital healthcare solutions is estimated to reach EUR 232 billion by 2025. Digital healthcare platforms are making transformative changes to conventional healthcare processes which can provide many beneficial improvements for both citizen and government provision to society. These benefits are obvious during pandemics such as Covid-19, when most healthcare services are offered through digital means. OBJECTIVE The objective of this study is to measure the role of trust and information quality when using digital healthcare platforms. These constructs are integrated with the Unified Theory of Acceptance and Use of Technology (UTAUT) to provide a better understanding of the consumer perspective regarding the use of digital healthcare platforms. METHODS Online structured self-administered questionnaire was utilized to collect the data. A sample consisting of 249 respondents participated in the questionnaire. Descriptive analysis was used to characterize the attributes of participants, and other statistical tests were conducted to ensure the reliability and validity of the survey. The model of the study was evaluated using Structural Equation Modelling (SEM) to explain the extent of the relationship among latent variables. RESULTS The study determined that facilitating conditions (t=0.233, p=0.023) and trust (t=0.324, p=0.005) had a significant impact on consumers' behavioral intention of using such platforms during Covid-19 pandemic. CONCLUSION This study highlighted the importance of facilitating conditions and trust factors for healthcare consumers of digital healthcare platforms especially during the pandemic time.
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Affiliation(s)
- Fawaz Alharbi
- Computer Science Department, Huraymila College of Science and Humanities Shaqra University, Saudi Arabia
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45
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Zhang G, Liu X. Prediction and control of COVID-19 spreading based on a hybrid intelligent model. PLoS One 2021; 16:e0246360. [PMID: 33571234 PMCID: PMC7877772 DOI: 10.1371/journal.pone.0246360] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/18/2021] [Indexed: 12/21/2022] Open
Abstract
The coronavirus (COVID-19) is a highly infectious disease that emerged in the late December 2019 in Wuhan, China. It caused a worldwide outbreak and a major threat to global health. It is important to design prediction and control strategies to restrain its exploding. In this study, a hybrid intelligent model is proposed to simulate the spreading of COVID-19. First, considering the effect of control measures, such as government investment, media publicity, medical treatment, and law enforcement in epidemic spreading. Then, the infection rates are optimized by genetic algorithm (GA) and a modified susceptible-infected-quarantined-recovered (SIQR) epidemic spreading model is proposed. In addition, the long short-term memory (LSTM) is imbedded into the SIQR model to design the hybrid intelligent model to further optimize other parameters of the system model, which can obtain the optimal predictive model and control measures. Simulation results show that the proposed hybrid intelligence algorithm has good predictive ability. This study provide a reliable model to predict cases of infection and death, and reasonable suggestion to control COVID-19.
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Affiliation(s)
- Gengpei Zhang
- China Three Gorges University, Yichang, Hubei, China
| | - Xiongding Liu
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China
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What Can COVID-19 Teach Us about Using AI in Pandemics? Healthcare (Basel) 2020; 8:healthcare8040527. [PMID: 33271960 PMCID: PMC7711608 DOI: 10.3390/healthcare8040527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/23/2020] [Accepted: 11/24/2020] [Indexed: 12/17/2022] Open
Abstract
The COVID-19 pandemic put significant strain on societies and their resources, with the healthcare system and workers being particularly affected. Artificial Intelligence (AI) offers the unique possibility of improving the response to a pandemic as it emerges and evolves. Here, we utilize the WHO framework of a pandemic evolution to analyze the various AI applications. Specifically, we analyzed AI from the perspective of all five domains of the WHO pandemic response. To effectively review the current scattered literature, we organized a sample of relevant literature from various professional and popular resources. The article concludes with a consideration of AI’s weaknesses as key factors affecting AI in future pandemic preparedness and response.
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