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  1. Article: Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM.

    Shahid, Farah / Zameer, Aneela / Muneeb, Muhammad

    Chaos, solitons, and fractals

    2020  Volume 140, Page(s) 110212

    Abstract: ... to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE ... for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM ... of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance ...

    Abstract COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.
    Keywords covid19
    Language English
    Publishing date 2020-08-19
    Publishing country England
    Document type Journal Article
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.110212
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

    Shahid, Farah / Zameer, Aneela / Muneeb, Muhammad

    Chaos, Solitons & Fractals

    2020  Volume 140, Page(s) 110212

    Keywords General Mathematics ; covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.110212
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: Predictions for COVID-19 with Deep Learning Models of LSTM, GRU and Bi-LSTM

    Shahid, Farah / Zameer, Aneela / Muneeb, Muhammad

    Chaos, Solitons & Fractals

    Abstract: ... to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA Bi-LSTM generates lowest MAE and RMSE ... for recovered cases in China On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM ... of cases, Bi-LSTM model outperforms in terms of endorsed indices Models ranking from good performance ...

    Abstract COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19 The performance of models is measured by mean absolute error, root mean square error and r2_score indices In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA Bi-LSTM generates lowest MAE and RMSE values of 0 0070 and 0 0077, respectively, for deaths in China The best r2_score value is 0 9997 for recovered cases in China On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #720454
    Database COVID19

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  4. Article: Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach.

    Zhou, Luyu / Zhao, Chun / Liu, Ning / Yao, Xingduo / Cheng, Zewei

    Engineering applications of artificial intelligence

    2023  Volume 122, Page(s) 106157

    Abstract: ... generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed ... of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM ... and we compared these proposed methods to other machine learning models to evaluate the performance ...

    Abstract Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data.
    Language English
    Publishing date 2023-03-16
    Publishing country England
    Document type Journal Article
    ISSN 0952-1976
    ISSN 0952-1976
    DOI 10.1016/j.engappai.2023.106157
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Time series forecasting of COVID-19 infections and deaths in Alpha and Delta variants using LSTM networks.

    Sheikhi, Farnaz / Kowsari, Zahra

    PloS one

    2023  Volume 18, Issue 10, Page(s) e0282624

    Abstract: ... the country of Iran, the continent of Asia, and the whole world. We propose four deep learning models based ... Gated Recurrent Unit (GRU). Performance of these models in predictions are evaluated using the root mean ... in transmissibility, severity of infections, and mortality rate. Designing models that are capable of predicting ...

    Abstract Since the beginning of the rapidly spreading COVID-19 pandemic, several mutations have occurred in the genetic sequence of the virus, resulting in emerging different variants of concern. These variants vary in transmissibility, severity of infections, and mortality rate. Designing models that are capable of predicting the future behavior of these variants in the societies can help decision makers and the healthcare system to design efficient health policies, and to be prepared with the sufficient medical devices and an adequate number of personnel to fight against this virus and the similar ones. Among variants of COVID-19, Alpha and Delta variants differ noticeably in the virus structures. In this paper, we study these variants in the geographical regions with different size, population densities, and social life styles. These regions include the country of Iran, the continent of Asia, and the whole world. We propose four deep learning models based on Long Short-Term Memory (LSTM), and examine their predictive power in forecasting the number of infections and deaths for the next three, next five, and next seven days in each variant. These models include Encoder Decoder LSTM (ED-LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional LSTM (Conv-LSTM), and Gated Recurrent Unit (GRU). Performance of these models in predictions are evaluated using the root mean square error, mean absolute error, and mean absolute percentage error. Then, the Friedman test is applied to find the leading model for predictions in all conditions. The results show that ED-LSTM is generally the leading model for predicting the number of infections and deaths for both variants of Alpha and Delta, with the ability to forecast long time intervals ahead.
    MeSH term(s) Humans ; COVID-19/epidemiology ; Pandemics ; SARS-CoV-2/genetics ; Time Factors ; Forecasting
    Language English
    Publishing date 2023-10-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0282624
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.

    Tariq, Muhammad Usman / Ismail, Shuhaida Binti / Babar, Muhammad / Ahmad, Ashir

    PloS one

    2023  Volume 18, Issue 7, Page(s) e0287755

    Abstract: ... of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold ... for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict ... approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate ...

    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.
    MeSH term(s) Humans ; COVID-19/epidemiology ; Deep Learning ; SARS-CoV-2 ; Africa ; Asia ; Forecasting
    Language English
    Publishing date 2023-07-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0287755
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Time series forecasting of COVID-19 infections and deaths in Alpha and Delta variants using LSTM networks.

    Farnaz Sheikhi / Zahra Kowsari

    PLoS ONE, Vol 18, Iss 10, p e

    2023  Volume 0282624

    Abstract: ... the country of Iran, the continent of Asia, and the whole world. We propose four deep learning models based ... Gated Recurrent Unit (GRU). Performance of these models in predictions are evaluated using the root mean ... in transmissibility, severity of infections, and mortality rate. Designing models that are capable of predicting ...

    Abstract Since the beginning of the rapidly spreading COVID-19 pandemic, several mutations have occurred in the genetic sequence of the virus, resulting in emerging different variants of concern. These variants vary in transmissibility, severity of infections, and mortality rate. Designing models that are capable of predicting the future behavior of these variants in the societies can help decision makers and the healthcare system to design efficient health policies, and to be prepared with the sufficient medical devices and an adequate number of personnel to fight against this virus and the similar ones. Among variants of COVID-19, Alpha and Delta variants differ noticeably in the virus structures. In this paper, we study these variants in the geographical regions with different size, population densities, and social life styles. These regions include the country of Iran, the continent of Asia, and the whole world. We propose four deep learning models based on Long Short-Term Memory (LSTM), and examine their predictive power in forecasting the number of infections and deaths for the next three, next five, and next seven days in each variant. These models include Encoder Decoder LSTM (ED-LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional LSTM (Conv-LSTM), and Gated Recurrent Unit (GRU). Performance of these models in predictions are evaluated using the root mean square error, mean absolute error, and mean absolute percentage error. Then, the Friedman test is applied to find the leading model for predictions in all conditions. The results show that ED-LSTM is generally the leading model for predicting the number of infections and deaths for both variants of Alpha and Delta, with the ability to forecast long time intervals ahead.
    Keywords Medicine ; R ; Science ; Q
    Subject code 519
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.

    Ayoobi, Nooshin / Sharifrazi, Danial / Alizadehsani, Roohallah / Shoeibi, Afshin / Gorriz, Juan M / Moosaei, Hossein / Khosravi, Abbas / Nahavandi, Saeid / Gholamzadeh Chofreh, Abdoulmohammad / Goni, Feybi Ariani / Klemeš, Jiří Jaromír / Mosavi, Amir

    Results in physics

    2021  Volume 27, Page(s) 104495

    Abstract: ... time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used ... methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate ... for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented ...

    Abstract The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.
    Language English
    Publishing date 2021-06-26
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2631798-9
    ISSN 2211-3797 ; 2211-3797
    ISSN (online) 2211-3797
    ISSN 2211-3797
    DOI 10.1016/j.rinp.2021.104495
    Database MEDical Literature Analysis and Retrieval System OnLINE

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