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.
ABSTRACT
More than 2 million people have died as a result of the COVID-19 outbreak. Angiotensin-converting enzyme 2 (ACE2) is a counter-regulatory enzyme that converts angiotensin-2 to Ang-(1-7) form in the renin-angiotensin system. Several studies have been analyzed the correlation between ACE2 and COVID-19. Indeed, ACE2/Ang (1-7) system protects the lung against acute respiratory distress syndrome by its anti-inflammatory/anti-oxidant function. However, SARS-Cov-2 can use ACE2 for host cell entry. Expression of ACE2 can be altered by several factors, including hypertension, diabetes and obesity, which also could increase the severity of COVID-19 infection. Besides, since androgens increase the expression of ACE-2, males are at higher risks of COVID-19 infection. Although reported statistics showed a significantly different infection risks of COVID-19 between adults and children, the reason behind the different responses is still unclear. This review proposes the effect of ACE polymorphism on the severity of SARS-COV-2 induced pneumonia. The previous meta-analysis regarding the effect of ACE polymorphism on the severity of pneumonia showed that polymorphism only affects the adult's illness severity and not the children. Two recent meta-analyses examined the effect of ACE polymorphism on the prevalence and mortality rate of COVID-19 and reported contradicting results. Our opinion paper suggests that the effect of ACE polymorphism on the severity of COVID-19 depends on the patients age, same as of the pneumonia.