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

Soudeh Ghafouri-Fard,Hossein Mohammad-Rahimi,Parisa Motie,Mohammad A.S Minabi,Mohammad Taheri,Saeedeh Nateghinia

doi:10.1016/j.heliyon.2021.e08143

Soudeh Ghafouri-Fard, Hossein Mohammad-Rahimi + Show 4 more

Open Access

https://doi.org/10.1016/j.heliyon.2021.e08143

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Abstract

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), R2 coefficient of determination (R2), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R2 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 R2 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.

Highlights

The novel coronavirus disease initiated in the late 2019 (COVID19) is resulted from the infection with the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) [1]
Several artificial intelligence strategies have been used for prediction of COVID-19 spread using different models (Figure 3)
Adaptive neuro-fuzzy inference system (ANFIS) is a type of artificial neural network being founded on Takagi–Sugeno fuzzy inference system

Summary

Introduction

The novel coronavirus disease initiated in the late 2019 (COVID19) is resulted from the infection with the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) [1]. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact for health systems and for policy makers. Policy making relies on discernments formed by 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 [2] application of artificial intelligence methods is an appropriate alternative to traditional epidemic models for prediction of its spread. Some traditional epidemic models such as Susceptible-Exposed-Infective-Recovery has been used for prediction of epidemic course [3], these methods have some limitations.

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