Results Various neurological manifestations have been reported in the literature associated with COVID-19, which in the current study are classified into Central Nervous System (CNS) related manifestations including headache, dizziness, impaired consciousness, acute cerebrovascular disease, epilepsy, and Peripheral Nervous System (PNS) related manifestations such as hyposmia/anosmia, hypogeusia/ageusia, muscle pain, and Guillain-Barre syndrome. Conclusion During the current context of COVID-19 pandemic, physicians should be aware of wide spectrum of neurological COVID-19 sign and symptoms for early diagnosis and isolation of patients. In this regard, COVID-19 has been associated with many neurological manifestations such as confusion, anosmia, and ageusia. Also, various evidences support the possible CNS roles in the COVID-19 pathophysiology. In this regard, further investigation of CNS involvement of SARS-COV-2 is suggested.

In December 2019, a new type of coronavirus was detected for the first time in Wuhan, Hubei Province, China. According to the reported data, the emerging coronavirus has spread worldwide, infecting more than fifty‐seven million individuals, leading to more than one million deaths. The current study aimed to review and discuss the hematological findings of COVID‐19. Laboratory changes and hematologic abnormalities have been reported repeatedly in COVID‐19 patients. WBC count and peripheral blood lymphocytes are normal or slightly reduced while these indicators may change with the progression of the disease. In addition, several studies demonstrated that decreased hemoglobin levels in COVID‐19 patients were associated with the severity of the disease. Moreover, thrombocytopenia, which is reported in 5%‐40% of patients, is known to be associated with poor prognosis of the disease. COVID‐19 can present with various hematologic manifestations. In this regard, accurate evaluation of laboratory indicators at the beginning and during COVID‐19 can help physicians to adjust appropriate treatment and provide special and prompt care for those in need.

Background Millions of people have been infected worldwide in the COVID-19 pandemic. In this study, we aim to propose fourteen prediction models based on artificial neural networks (ANN) to predict the COVID-19 outbreak for policy makers. Methods The ANN-based models were utilized to estimate the confirmed cases of COVID-19 in China, Japan, Singapore, Iran, Italy, South Africa and United States of America. These models exploit historical records of confirmed cases, while their main difference is the number of days that they assume to have impact on the estimation process. The COVID-19 data were divided into a train part and a test part. The former was used to train the ANN models, while the latter was utilized to compare the purposes. The data analysis shows not only significant fluctuations in the daily confirmed cases but also different ranges of total confirmed cases observed in the time interval considered. Results Based on the obtained results, the ANN-based model that takes into account the previous 14 days outperforms the other ones. This comparison reveals the importance of considering the maximum incubation period in predicting the COVID-19 outbreak. Comparing the ranges of determination coefficients indicates that the estimated results for Italy are the best one. Moreover, the predicted results for Iran achieved the ranges of [0.09, 0.15] and [0.21, 0.36] for the mean absolute relative errors and normalized root mean square errors, respectively, which were the best ranges obtained for these criteria among different countries. Conclusion Based on the achieved results, the ANN-based model that takes into account the previous fourteen days for prediction is suggested to predict daily confirmed cases, particularly in countries that have experienced the first peak of the COVID-19 outbreak. This study has not only proved the applicability of ANN-based model for prediction of the COVID-19 outbreak, but also showed that considering incubation period of SARS-COV-2 in prediction models may generate more accurate estimations.

Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a novel coronavirus that has infected more than 2,900,000 individuals worldwide. The widespread of coronavirus 2019 (COVID-19) brings about the need for a prediction model to adopt appropriate evidence-based strategies. In this study, multi-gene genetic programming (MGGP), as one of the artificial intelligence models, has been proposed for the first time for predicting the COVID-19 outbreak. Although this is a challenging task due to significant fluctuations of daily confirmed cases, the results achieved by MGGP are promising. To be more specific, the predicted confirmed cases are acceptably close to the observed values for seven countries considered in this study. Thus, MGGP is suggested for developing estimation models of COVID-19. Furthermore, similarities and differences between the proposed prediction models are presented. Finally, it is discussed why a country-based prediction model is recommended.

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