A COVID-19 time series forecasting model based on MLP ANN

Borghi, Pedro Henrique; Zakordonets, Oleksandr; Teixeira, João Paulo

doi:10.1016/j.procs.2021.01.250

Cited by 66 publications

(32 citation statements)

References 11 publications

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“…20201 India covid19india.org website Daily confirmed cases 30 January to 6 September 2020 LSTM - - - 0.03 [ 31 ] Behnood, A, et al, 2020 USA USAFacts Website Daily confirmed cases in 1657 counties ANFIS-VOA-II 22.4744 7.3337 0.8339 - [ 13 ] Borghi, P. H., et al. 2021 Global (top 30 countries with the highest number of daily new cases) Johns Hopkins University Center for Systems Science and Engineering dataset Daily confirmed cases till 11 May 2020 ANN 2.082E+03 3.718E+06 - - [ 32 ] Car, Z., et al., 2020 406 locations Johns Hopkins University Center for Systems Science and Engineering dataset Daily confirmed, recovered, and deceased patients in a certain location (defined by the name of location, latitude, and longitude), from 22 January to 12 March 2020 MLP - - 0.98599 - [ 5 ] Chakraborty, T., et al., 2020 USA Our World in Data Website Daily confirmed cases TARNN 721.5658 468.6335 - - [ 33 ] Brazil 178.0458 90.2053 India 201.0696 128.7718 Russia 443.4280 202.6083 South Africa 243.5067 160.3598 Mexico 24.4335 15.1298 Spain 136.3910 87.5449 Iran 319.9160 182.8744 Chakraborty, T., et al., 2020 Cana...…”

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

show abstract

“…The use of ANN to predict the number of cases and deaths for next 6 [4], 7 [5], 10 [6] and 40 [7] days was already experimented but for accumulative values, with promising result using a recursive approach and a variety of countries. The results of present work cannot be compared to results of previous similar works because the relative error of the accumulative cases cannot be directly compared with the absolute error of daily predictions.…”

Section: Discussionmentioning

confidence: 99%

“…Some authors already proposed by the use of time series forecasting applied to predict data about coronavirus propagation. In [4] the multilayer perceptron network is trained with thirty countries data using the previous 20 days of new and accumulative number of cases and deaths to predict accumulative cases and deaths for six days ahead. Similarly, the methodology proposed in [5] uses a combination of eight countries data of the accumulative number of cases and deaths applied to two training algorithms, Levenberg-Marquardt, and the Resilient Propagation to predict seven days of accumulative cases and deaths for Portugal, Brazil and USA.…”

Section: Introductionmentioning

confidence: 99%

Forecasted Incidence, Intensive Care Unit Admissions and Projected Mortality Attributable to Covid-19 in Portugal, UK, Germany, Italy and France – 4 Weeks Ahead

Carvalho¹,

Jp²,

Jakovljević³

et al. 2021

Preprint

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The use of Artificial Neural Networks (ANN) is a great contribution to medical studies since the application of forecasting concepts allows the analysis of future diseases propagations. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were proposed two methodologies to predict 28 days ahead the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, United Kingdom, and Germany, and a case study of the results of massive immunization in Israel. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries size, and the cumulative vaccination values by the percentage of population immunized, at least with one dose of vaccine. As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology and targets other possibilities of use for the proposed method. The best architecture achieved a general MAE for the 1 to 28 days ahead forecast lower than 30 cases, 0,6 deaths and 2,5 ICU patients by million people.

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“…Estimating the number of neurons and hidden layers for a neural network depends on the type of database samples for which the network is designed—the more suitable the estimate, the better results in increasing accuracy and reducing complexity and training time [30] . It has been shown that few resources are needed to predict low-randomness time series, such as series related to the number of infections and deaths caused by COVID-19 [22] .…”

Section: Methodsmentioning

confidence: 99%

“…Then, its accuracy in forecasting is compared with the MLP model using three metrics. The MLP and MC models have previously been used separately to predict various Covid-19 related indicators, e.g., in [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ], and [23] , the MLP model have been used. Besides, in [ 10 , 11 , 24 ], and [12] , the MC model has been used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model

Haghighat

2021

Chaos, Solitons & Fractals

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Although more than a year has passed since the coronavirus outbreak globally, the Covid-19 pandemic conditions still exist in many countries, including Iran. Predicting the number of future patients and deaths can help governments and policymakers make better decisions to enforce disease control restrictions. In this study, we aim to use a combined multilayer perceptron neural network (MLP) and Markov chain (MC) model to predict two indicators of the number of discharged and death cases according to their relationship with the number of hospitalized cases in Bushehr province, Iran. This hybrid model is called MLP-MC. In this study, 136 data (days) are collected from May 13, 2020, to April 1, 2021, divided into two parts: training and test. The training data are used to train the MLP network, and the trained MLP network is used to predict the test data and the next 40 days. Then the residual errors of actual and predicted values are calculated. In the next step, the MC model is used to classify the errors and predict the values of the indicators according to the probabilities related to the error states and improve the performance of the MLP model in forecasting. Finally, the prediction accuracy of MLP and MLP-MC models are compared using three evaluation metrics: MAD, MSE and RMSE. This comparison showed that the MLP-MC model has slightly higher prediction accuracy than the MLP model.

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A COVID-19 time series forecasting model based on MLP ANN

Cited by 66 publications

References 11 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

Forecasted Incidence, Intensive Care Unit Admissions and Projected Mortality Attributable to Covid-19 in Portugal, UK, Germany, Italy and France – 4 Weeks Ahead

Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model

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