Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases

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Abstract

The recent COVID-19 outbreak has severely affected people around the world. There is a need of an efficient decision making tool to improve awareness about the spread of COVID-19 infections among the common public. An accurate and reliable neural network based tool for predicting confirmed, recovered and death cases of COVID-19 can be very helpful to the health consultants for taking appropriate actions to control the outbreak. This paper proposes a novel Nonlinear Autoregressive (NAR) Neural Network Time Series (NAR-NNTS) model for forecasting COVID-19 cases. This NAR-NNTS model is trained with Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR) training algorithms. The performance of the proposed model has been compared by using Root Mean Square Error (RMSE), Mean Square Error (MSE) and correlation co-efficient i.e. R-value. The results show that NAR-NNTS model trained with LM training algorithm performs better than other models for COVID-19 epidemiological data prediction.

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Most cited references 48

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COVID-19 and Italy: what next?

Andrea Remuzzi, Giuseppe Remuzzi (2020)

Summary The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Although containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, and Italy has been particularly affected. There is now grave concern regarding the Italian national health system's capacity to effectively respond to the needs of patients who are infected and require intensive care for SARS-CoV-2 pneumonia. The percentage of patients in intensive care reported daily in Italy between March 1 and March 11, 2020, has consistently been between 9% and 11% of patients who are actively infected. The number of patients infected since Feb 21 in Italy closely follows an exponential trend. If this trend continues for 1 more week, there will be 30 000 infected patients. Intensive care units will then be at maximum capacity; up to 4000 hospital beds will be needed by mid-April, 2020. Our analysis might help political leaders and health authorities to allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the next few days and weeks. If the Italian outbreak follows a similar trend as in Hubei province, China, the number of newly infected patients could start to decrease within 3–4 days, departing from the exponential trend. However, this cannot currently be predicted because of differences between social distancing measures and the capacity to quickly build dedicated facilities in China.

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Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

Ioannis Apostolopoulos, Tzani Mpesiana (2020)

In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.

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Application of the ARIMA model on the COVID-2019 epidemic dataset

Domenico Benvenuto, Marta Giovanetti, Lazzaro Vassallo … (2020)

Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.

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Author and article information

Contributors

Suyel Namasudra: suyelnamasudra@gmail.com

R. Rathipriya: rathipriyar@gmail.com

Journal

Journal ID (nlm-ta): Neural Process Lett

Journal ID (iso-abbrev): Neural Process Lett

Title: Neural Processing Letters

Publisher: Springer US (New York )

ISSN (Print): 1370-4621

ISSN (Electronic): 1573-773X

Publication date (Electronic): 1 April 2021

Pages: 1-21

Affiliations

[1 ]GRID grid.444650.7, ISNI 0000 0004 1772 7273, Department of Computer Science and Engineering, , National Institute of Technology Patna, ; Bihar, India

[2 ]GRID grid.412490.a, ISNI 0000 0004 0538 1156, Department of Computer Science, , Periyar University, ; Salem, India

Article

Publisher ID: 10495

DOI: 10.1007/s11063-021-10495-w

PMC ID: 8012519

PubMed ID: 33821142

SO-VID: fb78a1a2-3e49-4999-bb46-77bc67824a58

License:

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases

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Novel Coronavirus Disease COVID-19

Most cited references 48

COVID-19 and Italy: what next?

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

Application of the ARIMA model on the COVID-2019 epidemic dataset

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