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Sluggish State-Based Neural Networks Provide State-of-the-art Forecasts of Covid-19 Cases

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Book cover Applied Intelligence and Informatics (AII 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1435))

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Abstract

At the time of writing, the Covid-19 pandemic is continuing to spread across the globe with more than 135 million confirmed cases and 2.9 million deaths across nearly 200 countries. The impact on global economies has been significant. For example, the Office for National Statistics reported that the UK’s unemployment level increased to 5% and the headline GDP declined by 9.9%, which is more than twice the fall in 2009 due to the financial crisis. It is therefore paramount for governments and policymakers to understand the spread of the disease, patient mortality rates and the impact of their interventions on these two factors. A number of researchers have subsequently applied various state-of-the-art forecasting models, such as long short-term memory models (LSTMs), to the problem of forecasting future numbers of Covid-19 cases (confirmed, deaths) with varying levels of success. In this paper, we present a model from the simple recurrent network class, The Multi-recurrent network (MRN), for predicting the future trend of Covid-19 confirmed and deaths cases in the United States. The MRN is a simple yet powerful alternative to LSTMs, which utilises a unique sluggish state-based memory mechanism. To test this mechanism, we first applied the MRN to predicting monthly Covid-19 cases between Feb 2020 to July 2020, which includes the first peak of the pandemic. The MRN is then applied to predicting cases on a weekly basis from late Feb 2020 to late Dec 2020 which includes two peaks. Our results show that the MRN is able to provide superior predictions to the LSTM with significantly fewer adjustable parameters. We attribute this performance to its robust sluggish state memory, lower model complexity and open up the case for simpler alternative models to the LSTM.

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Notes

  1. 1.

    The total number of COVID19 infected cases, total number of daily new cases, total number of deaths and total number of daily new deaths.

  2. 2.

    Logistic regression, Decision tree, Support vector machine, Naive Bayes, and Artificial neutral network.

  3. 3.

    The percentage of Covid-19 positive patients correctly identified by the models.

  4. 4.

    The percentage of Covid-19 negative patients correctly identified by the models.

  5. 5.

    Denmark, Belgium, Germany, France, United Kingdom, Finland, Switzerland and Turkey.

  6. 6.

    Bayesian regression neural network (BRNN), Cubist Regression (CUBIST), k-nearest neighbours (KNN), Quantile Random Forest (QRF), and support vector regression (SVR).

  7. 7.

    https://www.cdc.gov/.

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Author information

Authors and Affiliations

  1. School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK

    Oluwatamilore Orojo, Jonathan Tepper, T. M. McGinnity & Mufti Mahmud

  2. Perceptronix Ltd., Avon Way, Derby, DE65 5AE, UK

    Jonathan Tepper

  3. Intelligent Systems Research Centre, University of Ulster, Magee Campus, Derry, BT48 7JL, UK

    T. M. McGinnity

Authors
  1. Oluwatamilore Orojo
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  2. Jonathan Tepper
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  3. T. M. McGinnity
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  4. Mufti Mahmud
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Corresponding author

Correspondence to Oluwatamilore Orojo .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. Jahangirnagar University, Savar, Dhaka, Bangladesh

    M. Shamim Kaiser

  3. Auckland University of Technology, Auckland, New Zealand

    Nikola Kasabov

  4. Old Dominion University, Norfolk, VA, USA

    Khan Iftekharuddin

  5. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

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Orojo, O., Tepper, J., McGinnity, T.M., Mahmud, M. (2021). Sluggish State-Based Neural Networks Provide State-of-the-art Forecasts of Covid-19 Cases. In: Mahmud, M., Kaiser, M.S., Kasabov, N., Iftekharuddin, K., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-030-82269-9_30

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  • DOI: https://doi.org/10.1007/978-3-030-82269-9_30

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