Evaluation of the prediction of CoVID-19 recovered and unrecovered cases using symptoms and patient's meta data based on support vector machine, neural network, CHAID and QUEST Models.

OBJECTIVE: This paper aims to develop four prediction models for recovered and unrecovered cases using descriptive data of patients and symptoms of CoVID-19 patients. The developed prediction models aim to extract the important variables in predicting recovered cases by using the binary values for recovered cases. MATERIALS AND METHODS: The data were collected from different countries all over the world. The input of the prediction model contains 28 symptoms and four variables of the patient's information. Symptoms of COVID-19 include a high fever, low fever, sore throat, cough, and so on, where patient metadata includes Province, county, sex, and age. The dataset contains 1254 patients with 664 recovered cases. To develop prediction models, four models are used including neural network, support vector machine, CHAID, and QUEST models. To develop prediction models, the dataset is divided into train and test datasets with splitting ratios equal to 70%, and 30%, respectively. RESULTS: The results showed that the neural network model is the most effective model in developing COVID-19 prediction with the highest performance metrics using train and test datasets. The results found that recovered cases are associated with the place of the patients mainly, province of the patient. Besides the results showed that high fever is not strongly associated with recovered cases, where cough and low fever are strongly associated with recovered cases. In addition, the country, sex, and age of the patients have higher importance than other patient's symptoms in COVID-19 development. CONCLUSIONS: The results revealed that the prediction models of the recovered COVID-19 cases can be effectively predicted using patient characteristics and symptoms, besides the neural network model is the most effective model to create a COVID -19 prediction model. Finally, the research provides empirical evidence that recovered cases of COVID-19 are closely related to patients' provinces.

CoVID-19 symptoms analysis of deceased and recovered cases using Chi-square test.

This paper aims to show the relationship between COVID-19 symptoms and patients' status including recovered and deceased cases. The study uses different CoVID-19 patients' information from different countries, the dataset contains 13174 patients with 730 as recovered and 34 cases as deceased. The Chi-square test is adopted with asymptotic significance level to show the strength of each symptom on recovered and deceased cases independently. The study found that the recovered cases are associated with different symptoms based on the patient history, where the deceased cases showed that high fever is not responsible for increasing the number of deceased cases. In addition, the use of symptoms will not give evidence of the patients' status, and therefore gender, age, reason of infection and patients' province are more dominant in determining the status of patients.

Is visiting Qom spread CoVID-19 epidemic in the Middle East?

The CoVID-19 epidemic started in Wuhan, China and spread to 217 other countries around the world through direct contact with patients, goods transfer, animal transport, and touching unclean surfaces. In the Middle East, the first confirmed case in both Iran and UAE originated from China. A series of infections since those confirmed cases started in the Middle East originated from Qom, Iran, and other Shi'ite holy places. Thereafter, CoVID-19 has been transmitted to other countries in the Middle East. This report aims to trace all of the confirmed cases in the Middle East until March 6, 2020 and their further spread. This report proves that further transmission of CoVID-19 to the Middle East was because of human mobility, besides engaging in different Jewish and Shi'ite religious rites. This report suggests avoiding several religious rites, closing the borders of infected countries, and supporting the infected countries to prevent further transmission.

Are Italy and Iran really suffering from COVID-19 epidemic? A controversial study.

The number of global COVID-19 infected cases is increased rapidly to exceed 370 thousand. COVID-19 is transmitted between humans through direct contact and touching dirty surfaces. This paper aims to find the similarity between DNA sequences of COVID-19 in different countries, and to compare these sequences with three different diseases [HIV, Hand-Foot-Mouth disease (HFMD), and Cryptococcus]. The study used pairwise distance, maximum likelihood tree, and similarity between amino acid to find the results. The results showed that different three main types of viruses namely, COVID-19 are found. The virus in both Italy and Iran is not similar to COVID-19 in China and USA. While, two viruses were spread in Wuhan (before and after December 26, 2019). Besides Cryptococcus and HFMD are found as dominant diseases with Group 1 and Group 3, respectively. Authors claim that the current virus in Italy and Iran that killed thousands of people is not COVID-19 based on the available data.

The correlation between the spread of COVID-19 infections and weather variables in 30 Chinese provinces and the impact of Chinese government mitigation plans.

On February 1, 2020, China announced a novel coronavirus CoVID-19 outbreak to the public. CoVID-19 was classified as an epidemic by the World Health Organization (WHO). Although the disease was discovered and concentrated in Hubei Province, China, it was exported to all of the other Chinese provinces and spread globally. As of this writing, all plans have failed to contain the novel coronavirus disease, and it has continued to spread to the rest of the world. This study aimed to explore and interpret the effect of environmental and metrological variables on the spread of coronavirus disease in 30 provinces in China, as well as to investigate the impact of new China regulations and plans to mitigate further spread of infections. This article forecasts the size of the disease spreading based on time series forecasting. The growing size of CoVID-19 in China for the next 210 days is estimated by predicting the expected confirmed and recovered cases. The results revealed that weather conditions largely influence the spread of coronavirus in most of the Chinese provinces. This study has determined that increasing temperature and short-wave radiation would positively increase the number of confirmed cases, mortality rate, and recovered cases. The findings of this study agree with the results of our previous study.

A classifier prediction model to predict the status of Coronavirus COVID-19 patients in South Korea.

OBJECTIVE: Coronavirus COVID-19 further transmitted to several countries globally. The status of the infected cases can be determined basing on the treatment process along with several other factors. This research aims to build a classifier prediction model to predict the status of recovered and death coronavirus CovID-19 patients in South Korea. MATERIALS AND METHODS: Artificial neural network principle is used to classify the collected data between February 20, 2020 and March 9, 2020. The proposed classifier used different seven variables, namely, country, infection reason, sex, group, confirmation date, birth year, and region. The most effective variables on recovered and fatal cases are analyzed based on the neural network model. RESULTS: The results found that the proposed predictive classifier efficiently predicted recovered and death cases. Besides, it is found that discovering the infection reason would increase the probability to recover the patient. This indicates that the virus might be controllable based on infection reasons. In addition, the earlier discovery of the disease affords better control and a higher probability of being recovered. CONCLUSIONS: Our recommendation is to use this model to predict the status of the patients globally.

Intrapontomesencephalic colloid cyst: an unusual location. Case report.

Colloid cysts appear most commonly in the third ventricle; the occurrence of a colloid cyst in the brainstem is very unusual. The authors report on a patient with an intrapontomesencephalic colloid cyst. This 15-year-old girl complained of a headache associated with diplopia. Her neurological examination revealed right-sided sixth nerve paresis and a mild left hemiparesis. Radiological investigations revealed an intraparenchymal pontomesencephalic cystic mass. Surgical removal of the lesion was achieved via the pterional transsylvian approach and the patient experienced an excellent recovery. Histopathological examination revealed that the lesion was a typical colloid cyst. To the best of the authors' knowledge, this is the first case in which an intraparenchymal upper brainstem colloid cyst was surgically excised totally. In addition to describing this case, the authors also review other brainstem neuroepithelial cysts described in the literature and briefly discuss the concept of their origin.

Spinal angiolipoma: case report and review of literature.

Spinal extradural angiolipomas are distinct, benign, and rare lesions composed of mature lipocytes admixed with abnormal blood vessels. They account for 0.14% of all spinal axis tumors. The case described here was a 72-year-old patient presenting with a history of paraparesis, hypoesthesia under the T2 level, hyperreflexia, and urinary overflow incontinence that appeared within 7 days after the administration of a coronary vasodilator drug regimen. The spinal magnetic resonance scan showed a lipomatous mass with signal void lesions, suggesting a vascular component of the tumor. The patient improved rapidly after surgical resection of the epidural tumor and decompression of the cord. According to the present literature, the duration of neurological symptoms ranges from 1 to 180 months (mean 28 months). But this patient's neurological deterioration took place 4 days before hospitalization. We believe that this can be explained by the increased tumor blood volume caused by vasodilator drugs, which in turn exerted a pulsatile compressive effect on the cord.

Melatonin as a free radical scavenger in experimental head trauma.

Head trauma causes two kinds of injury in the neural tissue. One is the primary injury which occurs at the time of impact. The other one is a secondary injury and is a progressive process. Free radicals are produced during oxidative reactions formed after trauma. They have been thought to be responsible in the mechanism of the secondary injury. Some studies have been conducted to demonstrate the role of free oxygen radicals in neuronal injury. The alterations in the free radical level during the early posttraumatic period and the effect of a free radical scavenger on these alterations have not been studied as a whole. We aimed to demonstrate the free oxygen radical level changes in the early posttraumatic period and the effect of melatonin, which is a potent free radical scavenger, on the early posttraumatic free radical level. A two-staged experimental head trauma study was designed. In stage one, posttraumatic free radical level changes were determined. In the second stage, the effect of melatonin on the free radical level changes in the posttraumatic period was studied. Two main groups of rats each divided into four subgroups were studied. Rats in one of the main groups underwent severe head trauma, and malondealdehyde (MDA) levels were measured in the contused cerebral tissue at different time points. Rats in the other main group also underwent the same type of trauma, and melatonin was injected intraperitoneally at different time points after trauma. The MDA level alteration in the tissue was determined after the injection of melatonin. The MDA level increased rapidly in the early posttraumatic period. But in time, it decreased in the groups with only trauma. In the melatonin-treated group, the MDA level decreased after the injection of melatonin, when injected in the early posttraumatic period, compared to the control and trauma groups. However, melatonin increased MDA to a higher level than in the groups with only trauma and the control group when injected later than 2 h after trauma. The MDA level increases in the very early posttraumatic period of cerebral trauma and decreases in time. Melatonin, which is the most potent endogenous free radical scavenger, when injected intraperitoneally to the cerebral traumatized rats in the very early posttraumatic period, causes a significant decrease in the MDA level. But, melatonin, when injected more than 2 h after trauma, increases the MDA level in experimental cerebral trauma in rats.