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

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), 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.

Rigorous Policy-Making Amid COVID-19 and Beyond: Literature Review and Critical Insights

“…artificial intelligence (AI)-powered: incorporating intelligent and automatic decision-making mechanisms to ensure the policies are developed based on the most updated evidence [ 83 , 94 , 95 , 96 , 97 , 98 ];…”

“…AI can be understood as machine programs or algorithms that are “able to mimic human intelligence” [ 124 ]. The AI-powered component of the PADS model emphasizes the importance of incorporating intelligent and automatic decision-making mechanisms to ensure the policies are developed based on the most updated and comprehensive evidence robustly analyzed [ 83 , 94 , 95 , 96 , 97 , 98 ]. Advanced AI systems can help policymakers to make more informed policies that are both reactive (retrospectively analyzing data to develop intelligent solutions) and proactive (predictive decision-making insights based on advanced modelling) in nature [ 125 , 126 , 127 ].…”

Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control

“…Note that the linear Taylor approximation of xk+1 allowed us to express the nonquadratic term in the cost function (14) as follows:…”

“…), we looked for a sequence of deterministic values µ u k , state moments µ x k+1 , Σ x k+1 , and covariance matrices Σ xθ k+1 with Σ xθ 0 = 0, which solve (18), satisfy the input constraint (16), and minimize the cost (14). The free variables of the optimization are collected in (15).…”

“…Logistic wavelets were used in [11] to compute the possible cumulative number of people infected with COVID. The application of artificial intelligence and machine learning has also been successful in COVID-related modeling and prediction [12][13][14].…”