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Article: The impact of Russo-Ukrainian war, COVID-19, and oil prices on global food security.

Al-Rousan, Nadia / Al-Najjar, Hazem / Al-Najjar, Dana

2024 Volume 10, Issue 8, Page(s) e29279

Abstract: Context: Light of recent global upheavals, including volatile oil prices, the Russo-Ukrainian conflict, and the COVID-19 pandemic this study delves into their profound impact on the import and export dynamics of global foodstuffs. With rising staple ... ...

Abstract	Context: Light of recent global upheavals, including volatile oil prices, the Russo-Ukrainian conflict, and the COVID-19 pandemic this study delves into their profound impact on the import and export dynamics of global foodstuffs. With rising staple food prices reminiscent of the 2010-2011 global food crisis, understanding these shifts comprehensively is imperative. Objective: Our objective is to evaluate this impact by examining six independent variables (year, month, Brent crude oil, COVID-19, the Russo-Ukrainian conflict) alongside six food indicators as dependent variables. Employing Pearson's correlation, linear regression, and seasonal autoregressive integrated moving averages (SARIMA), we scrutinize intricate relationships among these variables. Results and conclusions: Our findings reveal varying degrees of association, notably highlighting a robust correlation between Brent crude oil and food indicators. Linear regression analysis suggests a positive influence of the Russo-Ukrainian conflict, Brent oil on food price indices, and COVID-19. Furthermore, integrating SARIMA enhances predictive accuracy, offering insights into future projections. Significance: Finally, this research has a significant role in providing a valuable analysis into the intricate dynamics of global food pricing, informing decision-making amidst global challenges and bridging critical gaps in prior research on forecasting food price indices.
Language	English
Publishing date	2024-04-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e29279
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Is visiting Qom spread CoVID-19 epidemic in the Middle East?

Al-Rousan, N / Al-Najjar, H

European review for medical and pharmacological sciences

2020 Volume 24, Issue 10, Page(s) 5813–5818

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Betacoronavirus/isolation & purification ; Betacoronavirus/physiology ; COVID-19 ; Cluster Analysis ; Coronavirus Infections/epidemiology ; Coronavirus Infections/pathology ; Coronavirus Infections/transmission ; Humans ; Middle East ; Pandemics ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/pathology ; Pneumonia, Viral/transmission ; Religion ; SARS-CoV-2 ; Travel
Keywords	covid19
Language	English
Publishing date	2020-06-04
Publishing country	Italy
Document type	Journal Article
ZDB-ID	605550-3
ISSN	2284-0729 ; 1128-3602 ; 0392-291X
ISSN (online)	2284-0729
ISSN	1128-3602 ; 0392-291X
DOI	10.26355/eurrev_202005_21376
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

Al-Rousan, N / Al-Najjar, H

European review for medical and pharmacological sciences

2020 Volume 24, Issue 8, Page(s) 4565–4571

Abstract: 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 ...

Abstract	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.
MeSH term(s)	Betacoronavirus ; COVID-19 ; China/epidemiology ; Coronavirus Infections/epidemiology ; Coronavirus Infections/mortality ; Forecasting ; Humans ; Infrared Rays ; Models, Theoretical ; Pandemics ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/mortality ; SARS-CoV-2 ; Temperature ; Weather ; Wind
Keywords	covid19
Language	English
Publishing date	2020-04-27
Publishing country	Italy
Document type	Journal Article
ZDB-ID	605550-3
ISSN	2284-0729 ; 1128-3602 ; 0392-291X
ISSN (online)	2284-0729
ISSN	1128-3602 ; 0392-291X
DOI	10.26355/eurrev_202004_21042
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Article ; Online: A classifier prediction model to predict the status of Coronavirus COVID-19 patients in South Korea.

Al-Najjar, H / Al-Rousan, N

European review for medical and pharmacological sciences

2020 Volume 24, Issue 6, Page(s) 3400–3403

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Betacoronavirus ; COVID-19 ; COVID-19 Testing ; Clinical Laboratory Techniques ; Coronavirus Infections/diagnosis ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Disease Outbreaks ; Health Education ; Humans ; Pandemics/prevention & control ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Republic of Korea/epidemiology ; SARS-CoV-2
Keywords	covid19
Language	English
Publishing date	2020-04-14
Publishing country	Italy
Document type	Journal Article
ZDB-ID	605550-3
ISSN	2284-0729 ; 1128-3602 ; 0392-291X
ISSN (online)	2284-0729
ISSN	1128-3602 ; 0392-291X
DOI	10.26355/eurrev_202003_20709
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Article ; Online: Are Italy and Iran really suffering from COVID-19 epidemic? A controversial study.

Al-Najjar, H / Al-Rousan, N

European review for medical and pharmacological sciences

2020 Volume 24, Issue 8, Page(s) 4519–4522

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Amino Acid Sequence ; Betacoronavirus/classification ; COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/virology ; Cryptococcus neoformans ; Enterovirus ; HIV ; Hand, Foot and Mouth Disease ; Humans ; Iran/epidemiology ; Italy/epidemiology ; Pandemics ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/virology ; SARS-CoV-2 ; Sequence Alignment ; Sequence Analysis, DNA
Keywords	covid19
Language	English
Publishing date	2020-04-27
Publishing country	Italy
Document type	Journal Article
ZDB-ID	605550-3
ISSN	2284-0729 ; 1128-3602 ; 0392-291X
ISSN (online)	2284-0729
ISSN	1128-3602 ; 0392-291X
DOI	10.26355/eurrev_202004_21034
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: 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.

Al-Najjar, D / Al-Najjar, H / Al-Rousan, N

European review for medical and pharmacological sciences

2021 Volume 25, Issue 17, Page(s) 5556–5560

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	COVID-19 ; Humans ; Metadata ; Models, Theoretical ; Neural Networks, Computer ; SARS-CoV-2 ; Support Vector Machine ; Symptom Assessment
Language	English
Publishing date	2021-09-01
Publishing country	Italy
Document type	Journal Article
ZDB-ID	605550-3
ISSN	2284-0729 ; 1128-3602 ; 0392-291X
ISSN (online)	2284-0729
ISSN	1128-3602 ; 0392-291X
DOI	10.26355/eurrev_202109_26668
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: CoVID-19 symptoms analysis of deceased and recovered cases using Chi-square test.

Al-Najjar, D / Al-Najjar, H / Al-Rousan, N

European review for medical and pharmacological sciences

2020 Volume 24, Issue 21, Page(s) 11428–11431

Abstract: 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 ...

Abstract	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.
MeSH term(s)	Age Factors ; Aged ; Aged, 80 and over ; Betacoronavirus/pathogenicity ; COVID-19 ; Chi-Square Distribution ; Coronavirus Infections/diagnosis ; Coronavirus Infections/mortality ; Data Analysis ; Datasets as Topic ; Female ; Humans ; Male ; Pandemics ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/mortality ; Prognosis ; Risk Assessment/methods ; Risk Factors ; SARS-CoV-2 ; Severity of Illness Index ; Sex Factors
Keywords	covid19
Language	English
Publishing date	2020-11-19
Publishing country	Italy
Document type	Journal Article
ZDB-ID	605550-3
ISSN	2284-0729 ; 1128-3602 ; 0392-291X
ISSN (online)	2284-0729
ISSN	1128-3602 ; 0392-291X
DOI	10.26355/eurrev_202011_23636
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

Al-Najjar, H / Al-Rousan, N

Eur Rev Med Pharmacol Sci

Abstract: 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 ... ...

Abstract	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.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #32271458
Database	COVID19

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

Al-Najjar, H / Al-Rousan, N

Eur Rev Med Pharmacol Sci

Abstract	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.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #32373989
Database	COVID19

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

Al-Rousan, N / Al-Najjar, H

Eur Rev Med Pharmacol Sci

Abstract	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.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #547472
Database	COVID19

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