A novel framework for COVID-19 case prediction through piecewise regression in India

Senapati, Apurbalal; Nag, Amitava; Mondal, Arunendu; Maji, Soumen

doi:10.1007/s41870-020-00552-3

Cited by 28 publications

(20 citation statements)

References 20 publications

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“…2021 Worldwide data Official WHO data Daily confirmed cases + Demographic, socioeconomic, and health sector indicators data Bidirectional LSTM 245.1 176.02 0.996 - [ 77 ] Saqib, M., 2020 US Johns Hopkins University Center for Systems Science and Engineering dataset Daily confirmed patients Hybrid polynomial-Bayesian ridge regression model 723.75 - - - [ 78 ] Italy 418.36 Spain 624.27 Senapati, A., et al. 2021 Assam (a state in India) Kaggle website Daily confirmed cases till 30 October 2020 Piecewise linear regression - - - 0.392 [ 79 ] Shyam Sunder Reddy, K., et al. 2020 India “Our World in Data” website Daily confirmed cases 15 February to 18 September 2020 LSTM 0.767 - - - [ 80 ] Tabar, B. R., et al., 2020 East Midlands region of England Public Health UK and NHS Digital Daily cumulative confirmed cases and the total number of daily phone calls received at the NHS 111 from 18 March to 19 September, 2020 MLR-T 19.37 14.16 - - [ 81 ] Torrealba-Rodriguez, O. et al., 2020 Mexico Daily Technical Report" by the Mexican Ministry of Health Daily confirmed cases from 27 February to 8 May, 2020 ANN - - 0.9999 - [ 82 ] Tuli, S., et al., 2020 World Our World in Data website Daily confirmed cases ...…”

Section: Table S1mentioning

confidence: 99%

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

Ghafouri‐Fard¹,

Mohammad‐Rahimi²,

Motie³

et al. 2021

Heliyon

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COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R 2 coefficient of determination (R 2 ), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R 2 values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R 2 values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.

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Section: Table S1mentioning

confidence: 99%

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

Ghafouri‐Fard¹,

Mohammad‐Rahimi²,

Motie³

et al. 2021

Heliyon

View full text Add to dashboard Cite

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“…The role played by regression analysis in data science cannot be overemphasised: predicting a continuous target is a pervasive task not only in practical terms, but also at a conceptual level. Regression is deeply investigated even nowadays, to the point of still being worth of considerations in top journals ( Jaqaman & Danuser, 2006 ; Altman & Krzywinski, 2015 ; Krzywinski & Altman, 2015 ), and widespread used also in the current scientific war against COVID-19 ( Chan et al, 2021 ; Raji & Lakshmi, 2020 ; Senapati et al, 2020 ; Gambhir et al, 2020 ). The theoretical basis of regression encompasses several aspects revealing hidden connections in the data and alternative perspectives even up to broadly speculative view: for instance, interpreting the whole statistical learning as a particular kind of regression ( Berk, 2020 ), or framing deep neural networks as recursive generalised regressors ( Wüthrich, 2020 ), or even provocatively pushing such considerations to the limit of setting the whole of statistics under the regression framework ( Hannay, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

Chicco

Warrens

Jurman

2021

PeerJ Computer Science

1,706

860

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Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R-squared or R2) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination (R-squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R-squared as standard metric to evaluate regression analyses in any scientific domain.

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“…Total number of confirmed and recovered cases has been predicated using linear regression model. Linear and exponential growth has been seen in the number of Covid-19 confirmed and recovered cases in India [ 15 ].…”

Section: Literature Reviewmentioning

confidence: 99%

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

et al. 2021

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The pandemic of novel coronavirus disease 2019 (Covid-19) has left the world to a standstill by creating a calamitous situation. To mitigate this devastating effect the inception of artificial intelligence into medical health care is mandatory. This study aims to present the educational perspective of Covid-19 and forecast the number of confirmed and death cases in the USA, India, and Brazil along with the discussion of endothelial dysfunction in epithelial cells and Angiotensin-Converting Enzyme 2 receptor (ACE2) with the Covid-19. Three different deep learning based experimental setups have been framed to forecast Covid-19. Models are (i) Bi-directional Long Short Term Memory (LSTM) (ii) Convolutional LSTM (iii) Proposed ensemble of Convolutional and Bi-directional LSTM network are known as CoBiD-Net ensemble. The educational perspective of Covid-19 has been given along with an architectural discussion of multi-organ failure due to intrusion of Covid-19 with the cell receptors of the human body. Different classification metrics have been calculated using all three models. Proposed CoBiD-Net ensemble model outperforms the other two models with respect to accuracy and mean absolute percentage error (MAPE). Using CoBiD-Net ensemble, accuracy for Covid-19 cases ranges from 98.10 to 99.13% with MAPE ranges from 0.87 to 1.90. This study will help the countries to know the severity of Covid-19 concerning education in the future along with forecasting of Covid-19 cases and human body interaction with the Covid-19 to make it the self-replicating phenomena. Supplementary Information The online version contains supplementary material available at 10.1007/s41324-021-00408-3.

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A novel framework for COVID-19 case prediction through piecewise regression in India

Cited by 28 publications

References 20 publications

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

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