scholarly journals Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

2020 ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Dengue Fever ◽  
Current Issue ◽  
World Health Organisation ◽  
Viral Disease ◽  
World Health ◽  
Dengue Outbreak ◽  
Depth Analysis ◽  
The World ◽  
Dengue Outbreaks ◽  
Vector Borne

Abstract Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction.

scholarly journals Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

2019 ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
World Health ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Depth Analysis ◽  
The World ◽  
Dengue Outbreaks ◽  
Machine Learning Models

Abstract Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. These factors were used as input parameters for machine learning models. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks.

scholarly journals Identification of significant climatic risk factors and machine learning models in dengue outbreak prediction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
Support Vector ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Bayes Network ◽  
The World ◽  
Dengue Outbreaks ◽  
Machine Learning Models

Abstract Background Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of 4-year data (January 2010 to December 2013) collected in Malaysia. Results This research has two major contributions. A new risk factor, called the TempeRain factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% for predicting dengue outbreaks. Conclusions This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

scholarly journals Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

2020 ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
Support Vector ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Bayes Network ◽  
The World ◽  
Dengue Outbreaks ◽  
Machine Learning Models

Abstract Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks. Conclusions: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

scholarly journals Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

2019 ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
World Health ◽  
Support Vector ◽  
Strong Impact ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Depth Analysis ◽  
Machine Learning Models

Abstract Background: Dengue fever is a widespread viral disease and one of the world’s main pandemic vector-borne infections and serious hazard to humanity. According to the World Health Organization (WHO), the incidence of dengue has grown dramatically worldwide in recent decades. The WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. Until today there is no tested vaccine or treatment to stop or prevent dengue fever thus the importance of dengue outbreak prediction is significant. The current issue in dengue outbreak prediction is accuracy. There are a limited number of studies that look at in depth analysis of climate factors in dengue outbreak prediction. Methods: In this study, the most significant and important climatic factors that contribute to dengue outbreak were identified. These factors were used as input parameters on machine learning models. The models were trained and evaluated based on four-year data from January 2010 to December 2013 in Malaysia. Results: This work provides two main contributions. A new risk factor, which was called TempeRain Factor (TRF), was determined and used as an input parameter for dengue prediction outbreak model. Moreover, the TRF was applied to demonstrate that its strong impact on dengue outbreaks. Experimental results showed that Support Vector Machine (SVM) with the newly identified meteorological risk factor in this study resulted in higher accuracy of 98.09% and reduced the root mean square error to 0.098 for predicting dengue outbreak. Conclusions: This research managed to explore on the factors that are being used in dengue outbreak prediction systems. The main contribution of this paper is in identifying new significant factors that contribute in dengue outbreak prediction. From the evaluation, we managed to obtain a significant improvement in accuracy of the machine-learning model in dengue outbreak prediction.

scholarly journals Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

2020 ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
Support Vector ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Bayes Network ◽  
The World ◽  
Dengue Outbreaks ◽  
Machine Learning Models

Abstract Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks. Conclusions: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

scholarly journals Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

2020 ◽  
Author(s):  
Felestin Yavari Nejad ◽  
Kasturi Dewi Varathan
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Dengue Fever ◽  
Support Vector ◽  
Learning Models ◽  
Dengue Outbreak ◽  
Bayes Network ◽  
The World ◽  
Dengue Outbreaks ◽  
Machine Learning Models

Abstract Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks. Conclusions: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

A Collaborative Study to Establish the Second International Standard for Streptokinase

1990 ◽  
Vol 64 (02) ◽  
pp. 267-269 ◽  
Author(s):  
A B Heath ◽  
P J Gaffney
Keyword(s):  
High Purity ◽  
Collaborative Study ◽  
World Health Organisation ◽  
World Health ◽  
Clot Lysis ◽  
Current Standard ◽  
International Standard ◽  
Accelerated Degradation ◽  
The World ◽  
International Collaborative Study

SummaryAn International Standard for Streptokinase - Streptodomase (62/7) has been used to calibrate high purity clinical batches of SK since 1965. An international collaborative study, involving six laboratories, was undertaken to replace this standard with a high purity standard for SK. Two candidate preparations (88/826 and 88/824) were compared by a clot lysis assay with the current standard (62/7). Potencies of 671 i.u. and 461 i.u. were established for preparations A (88/826) and B (88/824), respectively.Either preparation appeared suitable to serve as a standard for SK. However, each ampoule of preparation A (88/826) contains a more appropriate amount of SK activity for potency testing, and is therefore preferred. Accelerated degradation tests indicate that preparation A (88/826) is very stable.The high purity streptokinase preparation, coded 88/826, has been established by the World Health Organisation as the 2nd International Standard for Streptokinase, with an assigned potency of 700 i.u. per ampoule.

Comparative docking of SARS-CoV-2 receptors antagonists from repurposing drugs

2020 ◽  
Author(s):  
Micael Davi Lima de Oliveira ◽  
Kelson Mota Teixeira de Oliveira
Keyword(s):  
World Health Organisation ◽  
World Health ◽  
Lung Cells ◽  
The Novel ◽  
High Modulation ◽  
Main Protease ◽  
The World ◽  
Novel Coronavirus ◽  
Viral Receptors ◽  
Theoretical Results

According to the World Health Organisation, until 16 June, 2020, the number of confirmed and notified cases of COVID-19 has already exceeded 7.9 million with approximately 434 thousand deaths worldwide. This research aimed to find repurposing antagonists, that may inhibit the activity of the main protease (Mpro) of the SARS-CoV-2 virus, as well as partially modulate the ACE2 receptors largely found in lung cells, and reduce viral replication by inhibiting Nsp12 RNA polymerase. Docking molecular simulations were performed among a total of 60 structures, most of all, published in the literature against the novel coronavirus. The theoretical results indicated that, in comparative terms, paritaprevir, ivermectin, ledipasvir, and simeprevir, are among the most theoretical promising drugs in remission of symptoms from the disease. Furthermore, also corroborate indinavir to the high modulation in viral receptors. The second group of promising drugs includes remdesivir and azithromycin. The repurposing drugs HCQ and chloroquine were not effective in comparative terms to other drugs, as monotherapies, against SARS-CoV-2 infection.

Use of Phosphatidylcholine in the Treatment of Localized Fat Deposits. Results and expectations

2017 ◽  
Vol 68 (7) ◽  
pp. 1438-1441 ◽  
Author(s):  
Sorin Berbece ◽  
Dan Iliescu ◽  
Valeriu Ardeleanu ◽  
Alexandru Nicolau ◽  
Radu Cristian Jecan
Keyword(s):  
World Health Organisation ◽  
Normal Weight ◽  
World Health ◽  
Basic Substance ◽  
Physical Exercises ◽  
Fat Deposits ◽  
The World ◽  
The Common ◽  
Global Health Problem ◽  
Over Time

Obesity represents a global health problem. According to the latest studies released by the World Health Organisation (WHO), 1.7 billion currently in excess of normal weight individuals, of which approx. 75% are overweight (body mass index - BMI 25 to 30). The common form of excess adipose tissue manifestation in overweight individuals is localized fat deposits with high (abdominal) or low (buttocks and thighs) disposition. Although the overweight can be corrected relatively easy by changing behavioral habits or food, a constant physical exercises program or following a diet food are not accessible to all through the efforts of will, financial and time involved. Several methods have been studied and tested over time to eliminate more or less invasive fat deposits with varying efficacy and adverse effects. Chemical lipolysis using phosphatidylcholine as the basic substance was initially used in hypercholesterolemia and its complications and was rapidly adopted in mesotherapy techniques for the treatment of fat deposits. This study reveals the results obtained using Dermastabilon on a sample of 16 patients, the time allocated to treatment and discomfort being minimal, and rapid and notable results. There were no side effects.

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