scholarly journals COVID-19 Real-Time Tracker and Analytical Report

2020 ◽  
Author(s):  
Jiawei Long
Keyword(s):  
Public Health ◽  
Real Time ◽  
Regression Models ◽  
Data Retrieval ◽  
Global Public Health ◽  
Time Data ◽  
General Audience ◽  
The Real ◽  
Logistic Regression Models ◽  
Data Visualizations

While the COVID-19 outbreak was reported to first originate from Wuhan, China, it has been declared as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 by WHO, and it has spread to over 180 countries by the time of this paper was being composed. As the disease spreads around the globe, it has evolved into a worldwide pandemic, endangering the state of global public health and becoming a serious threat to the global community. To combat and prevent the spread of the disease, all individuals should be well-informed of the rapidly changing state of COVID-19. In the endeavor of accomplishing this objective, a COVID-19 real-time analytical tracker has been built to provide the latest status of the disease and relevant analytical insights. The real-time tracker is designed to cater to the general audience without advanced statistical aptitude. It aims to communicate insights through various straightforward and concise data visualizations that are supported by sound statistical foundations and reliable data sources. This paper aims to discuss the major methodologies which are utilized to generate the insights displayed on the real-time tracker, which include real-time data retrieval, normalization techniques, ARIMA time-series forecasting, and logistic regression models. In addition to introducing the details and motivations of the utilized methodologies, the paper additionally features some key discoveries that have been derived in regard to COVID-19 using the methodologies.

scholarly journals Analyzing the Epidemiological Outbreak of COVID-19: Real-time, Visual Data Analysis, Short-term Forecasting, and Risk Factor Identification

2021 ◽  
Vol 2 (3) ◽  
pp. 246-261
Author(s):  
Jiawei Long
Keyword(s):  
Public Health ◽  
Language Processing ◽  
Moving Average ◽  
Global Public Health ◽  
Automatic Data ◽  
Logistic Regression Models ◽  
Data Ingestion ◽  
Short Term Forecasting ◽  
Processing Techniques ◽  
Data Visualizations

The COVID-19 outbreak was initially reported in Wuhan, China, and it has been declared as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 by WHO. It has now spread to over 180 countries, and it has gradually evolved into a world-wide pandemic, endangering the state of global public health and becoming a serious threat to the global community. To combat and prevent the spread of the disease, all individuals should be well-informed of the rapidly changing state of COVID-19. To accomplish this objective, I have built a website to analyze and deliver the latest state of the disease and relevant analytical insights. The website is designed to cater to the general audience, and it aims to communicate insights through various straightforward and concise data visualizations that are supported by sound statistical methods, accurate data modeling, state-of-the-art natural language processing techniques, and reliable data sources. This paper discusses the major methodologies which are utilized to generate the insights displayed on the website, which include an automatic data ingestion pipeline, normalization techniques, moving average computation, ARIMA time-series forecasting, and logistic regression models. In addition, the paper highlights key discoveries that have been derived in regard to COVID-19 using the methodologies. Doi: 10.28991/HIJ-2021-02-03-09 Full Text: PDF

scholarly journals Variants in SARS-CoV-2 Associated with Mild or Severe Outcome

2020 ◽  
Author(s):  
Jameson D. Voss ◽  
Martin Skarzynski ◽  
Erin M. McAuley ◽  
Ezekiel J. Maier ◽  
Thomas Gibbons ◽  
...  
Keyword(s):  
Public Health ◽  
Regression Models ◽  
Molecular Characteristics ◽  
Median Frequency ◽  
Global Public Health ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Logistic Regression Models ◽  
Health Measures ◽  
And Gender

AbstractIntroductionThe coronavirus disease 2019 (COVID-19) pandemic is a global public health emergency causing a disparate burden of death and disability around the world. The molecular characteristics of the virus that predict better or worse outcome are largely still being discovered.MethodsWe downloaded 155,958 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from GISAID and evaluated whether variants improved prediction of reported severity beyond age and region. We also evaluated specific variants to determine the magnitude of association with severity and the frequency of these variants among the genomes.ResultsLogistic regression models that included viral genomic variants outperformed other models (AUC=0.91 as compared with 0.68 for age and gender alone; p<0.001). Among individual variants, we found 17 single nucleotide variants in SARS-CoV-2 have more than two-fold greater odds of being associated with higher severity and 67 variants associated with ≤ 0.5 times the odds of severity. The median frequency of associated variants was 0.15% (interquartile range 0.09%-0.45%). Altogether 85% of genomes had at least one variant associated with patient outcome.ConclusionNumerous SARS-CoV-2 variants have two-fold or greater association with odds of mild or severe outcome and collectively, these variants are common. In addition to comprehensive mitigation efforts, public health measures should be prioritized to control the more severe manifestations of COVID-19 and the transmission chains linked to these severe cases.

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

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.

Beverage intake and obesity in early childhood: evidence form primary health care clients in Northwest Argentina

2015 ◽  
Vol 7 (3) ◽  
pp. 244-252 ◽  
Author(s):  
E. Alderete ◽  
I. Bejarano ◽  
A. Rodríguez
Keyword(s):  
Public Health ◽  
Nutritional Status ◽  
Regression Models ◽  
Task Force ◽  
Normal Weight ◽  
The Body ◽  
Logistic Regression Models ◽  
Dietary Recall ◽  
Northwest Argentina ◽  
Artificial Fruit

Sugar sweetened beverages (SSB) are thought to play an important role in weight gain. We examined the relationship between the intake of caloric and noncaloric beverages (SSB and water) and the nutritional status of children. In 2014, we randomly selected 16 public health clinics in four cities of Northwest Argentina and conducted a survey among mothers of children 0–6 years of age. Children’s beverage intake was ascertained by 24-h dietary recall provided by the mothers. Children’s weight and height measures were obtained from clinic’s registries. We calculated the body mass index using the International Obesity Task Force standards. The analysis included 562 children 25 months to 6 years of age with normal or above normal nutritional status. Children’s beverage consumption was as follows, water 81.8%, carbonated soft drinks (CSD) 49.7%, coffee/tea/cocoa 44.0%, artificial fruit drinks 35.6%, flavored water 17.9%, natural fruit juice 14.5%. In multivariate logistic regression models the likelihood of being obese v. being overweight or having normal weight doubled with an intake of one to five glasses of CSD (OR=2.2) and increased by more than three-fold with an intake of more than five glasses (OR=3.5). Drinking more than five glasses of water decreased the likelihood of being obese by less than half (OR=0.3). The percentage of children drinking more than five glasses of other beverages was low (3.3–0.9%) and regression models did not yield significant results. The study contributed evidence for reducing children’s CSD intake and for promoting water consumption, together with the implementation of comprehensive regulatory public health policies.

scholarly journals Using Digital Twining in Fast-food Production Chain Simulation

2021 ◽  
Vol 343 ◽  
pp. 03005
Author(s):  
Florina Chiscop ◽  
Bogdan Necula ◽  
Carmen Cristiana Cazacu ◽  
Cristian Eugen Stoica
Keyword(s):  
Real Time ◽  
Fast Food ◽  
Virtual Model ◽  
Time Data ◽  
Production Chain ◽  
The Real ◽  
Time Dynamic ◽  
Digital Twin ◽  
Food Company ◽  
The Moment

The topic of this paper represents our research in the process of creating a virtual model (digital twin) for a fast-food company production chain starting with the moment when a customer launches an order, following with the processing of that order, until the customer receives it. The model will describe elements that are included in this process such as equipment, human resources and the necessary space that is needed to host this layout. The virtual model created in a simulation platform will be a replicate of a real fast-food company, thus helping us observe the real time dynamic of this production system. Using WITNESS HORIZON 23 we will construct the model of the layout based on real time data received from the fast-food company. This digital twin will be used to manage the production chain material flow, evaluating the performance of the system architecture in various scenarios. In order to obtain a diagnosis of the system’s performance we will simulate the workflow running through preliminary architecture in compliance with the real time behaviour to identify the bottlenecks and blockages in the flow trajectory. In the end we will propose two different optimised architectures for the fast-food company production chain.

Customer behavior analysis using real-time data processing

2019 ◽  
Vol 31 (1) ◽  
pp. 265-290 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Fazal Ijaz ◽  
Muhammad Syafrudin ◽  
M. Alex Syaekhoni ◽  
Norma Latif Fitriyani ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Association Rule ◽  
Customer Behavior ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Big Data Technology

PurposeThe purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.Design/methodology/approachIn order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.FindingsFirst, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.Research limitations/implicationsThis research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.Practical implicationsThe proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.Social implicationsThe proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.Originality/valueThe key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

Cassandra-based data repository design for food supply chain traceability

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandeep Kumar Singh ◽  
Mamata Jenamani
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Query Language ◽  
Data Repository ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Traceability System ◽  
Real Time Data

Purpose The purpose of this paper is to design a supply chain database schema for Cassandra to store real-time data generated by Radio Frequency IDentification technology in a traceability system. Design/methodology/approach The real-time data generated in such traceability systems are of high frequency and volume, making it difficult to handle by traditional relational database technologies. To overcome this difficulty, a NoSQL database repository based on Casandra is proposed. The efficacy of the proposed schema is compared with two such databases, document-based MongoDB and column family-based Cassandra, which are suitable for storing traceability data. Findings The proposed Cassandra-based data repository outperforms the traditional Structured Query Language-based and MongoDB system from the literature in terms of concurrent reading, and works at par with respect to writing and updating of tracing queries. Originality/value The proposed schema is able to store the real-time data generated in a supply chain with low latency. To test the performance of the Cassandra-based data repository, a test-bed is designed in the lab and supply chain operations of Indian Public Distribution System are simulated to generate data.

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