Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2017 ◽  
Vol 8 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

Web Services ◽  
2019 ◽  
pp. 490-509 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2018 ◽  
pp. 427-446 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

scholarly journals Spatiotemporal analysis of the first wave of COVID-19 hospitalisations in Birmingham, UK

BMJ Open ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. e050574
Author(s):  
Samuel I Watson ◽  
Peter J Diggle ◽  
Michael G Chipeta ◽  
Richard J Lilford
Keyword(s):  
Disease Control ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Temporal Correlation ◽  
Spatiotemporal Analysis ◽  
Control Measures ◽  
University Hospitals ◽  
Disease Surveillance System ◽  
Local Areas

ObjectivesTo evaluate the spatiotemporal distribution of the incidence of COVID-19 hospitalisations in Birmingham, UK during the first wave of the pandemic to support the design of public health disease control policies.DesignA geospatial statistical model was estimated as part of a real-time disease surveillance system to predict local daily incidence of COVID-19.ParticipantsAll hospitalisations for COVID-19 to University Hospitals Birmingham NHS Foundation Trust between 1 February 2020 and 30 September 2020.Outcome measuresPredictions of the incidence and cumulative incidence of COVID-19 hospitalisations in local areas, its weekly change and identification of predictive covariates.ResultsPeak hospitalisations occurred in the first and second weeks of April 2020 with significant variation in incidence and incidence rate ratios across the city. Population age, ethnicity and socioeconomic deprivation were strong predictors of local incidence. Hospitalisations demonstrated strong day of the week effects with fewer hospitalisations (10%–20% less) at the weekend. There was low temporal correlation in unexplained variance. By day 50 at the end of the first lockdown period, the top 2.5% of small areas had experienced five times as many cases per 10 000 population as the bottom 2.5%.ConclusionsLocal demographic factors were strong predictors of relative levels of incidence and can be used to target local areas for disease control measures. The real-time disease surveillance system provides a useful complement to other surveillance approaches by producing real-time, quantitative and probabilistic summaries of key outcomes at fine spatial resolution to inform disease control programmes.

scholarly journals Electronic Integrated Disease Surveillance System and Pathogen Asset Control System

2012 ◽  
Vol 79 (2) ◽  
Author(s):  
Tom G. Wahl ◽  
Aleksey V. Burdakov ◽  
Andrey O. Oukharov ◽  
Azamat K. Zhilokov
Keyword(s):  
Control System ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Data Exchange ◽  
Laboratory Data ◽  
World Health ◽  
Open Architecture ◽  
Case Definitions ◽  
Disease Surveillance System

Electronic Integrated Disease Surveillance System (EIDSS) has been used to strengthen and support monitoring and prevention of dangerous diseases within One Health concept by integrating veterinary and human surveillance, passive and active approaches, case-based records including disease-specific clinical data based on standardised case definitions and aggregated data, laboratory data including sample tracking linked to each case and event with test results and epidemiological investigations. Information was collected and shared in secure way by different means: through the distributed nodes which are continuously synchronised amongst each other, through the web service, through the handheld devices. Electronic Integrated Disease Surveillance System provided near real time information flow that has been then disseminated to the appropriate organisations in a timely manner. It has been used for comprehensive analysis and visualisation capabilities including real time mapping of case events as these unfold enhancing decision making. Electronic Integrated Disease Surveillance System facilitated countries to comply with the IHR 2005 requirements through a data transfer module reporting diseases electronically to the World Health Organisation (WHO) data center as well as establish authorised data exchange with other electronic system using Open Architecture approach. Pathogen Asset Control System (PACS) has been used for accounting, management and control of biological agent stocks. Information on samples and strains of any kind throughout their entire lifecycle has been tracked in a comprehensive and flexible solution PACS.Both systems have been used in a combination and individually. Electronic Integrated Disease Surveillance System and PACS are currently deployed in the Republics of Kazakhstan, Georgia and Azerbaijan as a part of the Cooperative Biological Engagement Program (CBEP) sponsored by the US Defense Threat Reduction Agency (DTRA).

scholarly journals Success in Revitalizing Weekly Disease Surveillance System in Zimbabwe Using Cell-phone Mediated Data Transmission, 2009-2013

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Henry Chidawanyika ◽  
Ponesai Nyika ◽  
Joshua Katiyo ◽  
Anthony Sox ◽  
Tongai Chokuda ◽  
...  
Keyword(s):  
Real Time ◽  
Data Transmission ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Cell Phone ◽  
Cell Phones ◽  
Decision Makers ◽  
Surveillance Data ◽  
Innovative Approach ◽  
Disease Surveillance System

Innovative approach to revitalizing Disease Surveillance System in Zimbabwe using cell-phone mediated data transmission has been a huge success. Cell phones have been successfully integrated into disease surveillance system resulting in expansion of surveillance coverage, improved completeness and timeliness. Decision makers are now able to access disease surveillance data in near real-time.

The Real Time Big Data Processing Framework Advantages and Limitations

2017 ◽  
Vol 5 (12) ◽  
pp. 305-312 ◽  
Author(s):  
Vairaprakash Gurusamy ◽  
◽  
◽  
◽  
S. Kannan ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Big Data Processing ◽  
The Real ◽  
Processing Framework

scholarly journals Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2946 ◽  
Author(s):  
Muhammad Syafrudin ◽  
Ganjar Alfian ◽  
Norma Fitriyani ◽  
Jongtae Rhee
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Processing ◽  
Prediction Model ◽  
Real Time ◽  
Manufacturing Process ◽  
Sensor Data ◽  
Big Data Processing ◽  
Automotive Manufacturing ◽  
Hybrid Prediction

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

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