Using Quasi Real-Time Data from the World-Wide Web

The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. Using a real-time data from 2 March 2020 to 15 May 2020 collected from Saudi Ministry of Health, we aimed to give a local prediction of the epidemic in Saudi Arabia. We used two models: the Logistic Growth and the Susceptible-Infected-Recovered for real-time forecasting the confirmed cases of COVID-19 across Saudi Arabia. Our models predicted that the epidemics of COVID-19 will have total cases of 69,000 to 79,000 cases. The simulations also predicted that the outbreak will entering the final-phase by end of June 2020.

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Real-time interactive virtual tour on the World Wide Web (WWW)

10.1117/12.526890 ◽

2003 ◽

Author(s):

Sanghyuk Yoon ◽

Hai-jung Chen ◽

Tom Hsu ◽

Ilmi Yoon

Keyword(s):

World Wide Web ◽

Real Time ◽

World Wide ◽

Virtual Tour ◽

The World

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Frontiers of Real-Time Data Analysis

Journal of Economic Literature ◽

10.1257/jel.49.1.72 ◽

2011 ◽

Vol 49 (1) ◽

pp. 72-100 ◽

Cited By ~ 143

Author(s):

Dean Croushore

Keyword(s):

Data Analysis ◽

Real Time ◽

Data Sets ◽

Current Analysis ◽

Time Data ◽

The Past ◽

Data Revisions ◽

The World ◽

Real Time Data ◽

The Impact

In the past ten years, researchers have explored the impact of data revisions in many different contexts. Researchers have examined the properties of data revisions, how structural modeling is affected by data revisions, how data revisions affect forecasting, the impact of data revisions on monetary policy analysis, and the use of real-time data in current analysis. This paper summarizes many of the questions for which real-time data analysis has provided answers. In addition, researchers and institutions have developed better real-time data sets around the world. Still, additional research is needed in key areas and research to date has uncovered even more fruitful areas worth exploring. (JEL C52, C53, C80, E01)

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A Continuous Data Production Approach to Flow Estimation in Canada

10.5194/egusphere-egu21-8897 ◽

2021 ◽

Author(s):

Francois Rainville ◽

Alain Pietroniro ◽

Andre Bouchard ◽

Amber Brown ◽

Douglas Stiff

Keyword(s):

Real Time ◽

Production System ◽

Water Levels ◽

Continuous Data ◽

Production Cycle ◽

Time Data ◽

The World ◽

Data Production ◽

Real Time Data ◽

Rating Curves

<p>The world has entered an era of immense water-related threats due to climate warming and human actions.&#160; Changing precipitation patterns, reducing snowpack, accelerating glacial melt, intensifying floods and droughts have made the need for timely hydrometric information indispensable. Climate change thus introduced requirements for adaptive management and timely water resource information at the municipal, regional and national levels. Over the last 10 years, it became evident that demands from users had moved towards best available hydrometric data in near real-time.&#160; As with most hydrometric services around the world, the WSC was a legacy and archive-driven organization that published approved data on an annual basis.&#160; Real-time data was an after-thought simply equated with the application of rating curves onto telemetry water levels, while hydrographers remained focused on approving data months after the facts. To address this challenge, the Meteorological Service of Canada&#8216;s National Hydrological Services, and specifically the Water Survey of Canada (WSC) has developed a near real-time continuous data production system to meet the evolving needs of stakeholders.&#160; To meet this challenge, WSC developed solutions where data would be improved as field-measurements were being acquired. Corrections to data and rating curves are applied within hours of field discharge measurements, allowing for near-real time publication of corrected discharge information.&#160; Moreover, station conditions and performance are constantly monitored with &#8220;eyes-on-data&#8221; production tools that allow the program to optimize its field visits, costs and data publication. These tools were developed in-house to enable effective network time-management while communicating important information with partner agencies.&#160; This was made possible with a cloud-based hydrometric data production system and modern telecommunications tools.&#160; As a result of this work, the improved near real-time data became the catalyst to revamp a multi-decade approach to final data approval. This improved overall efficiency and is now leading to less delays in the approved data production cycle. &#160;This paper describes the design and implementation of the continuous data production system adopted at WSC and highlights some of the benefits noted since program implementation. This paper also identifies future investments that could help the sustainability of this new system in the long term.</p>

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Delayed dynamics of SIR model for COVID-19 spread forecasting

10.21203/rs.3.rs-101284/v1 ◽

2020 ◽

Author(s):

Hameed K. Ebraheem ◽

Nizar Alkhateeb ◽

Hussein Badran ◽

Ali Hajjiah ◽

Ebraheem Sultan

Keyword(s):

Real Time ◽

Geographical Area ◽

Sir Model ◽

Accurate Prediction ◽

World Health ◽

Reproductive Number ◽

Time Data ◽

New Model ◽

The World ◽

Real Time Data

Abstract BackgroundThe global spread of the COVID-19 pandemic has been one of the most challenging tasks the world has faced since the last pandemic outbreak of 1918. Early on countries felt the strength and persistence of the virus infections spreading with no means of estimating the dispersion rates. Officials in infected countries followed several guidelines set by the World Health Organization (WHO) to try and flatten the infection curve and maintain a low number of infectives. Nonetheless, the virus kept on spreading with impunity and all predictions of how containments or peak detections have been a fail so far. Therefore, a need for a more accurate model to predict the peaking of infections and help guide officials on what best to enact as a measure of public health safety from a multitude of choices outlined by the WHO. Earlier studies of compartmental model of Susceptible-Infected-Recovered (SIR) did not predict the peaking of a hot spots flairs of viral infections and a new model needed to provide a more realistic results to serve public officials battling the pandemic worldwideMethodsA new modified SIR model which incorporates appropriate delay parameters leading to a more precise prediction of COVID-19 real time data. The predictions of the new model are compared to real data obtained from four countries, namely Germany, Italy, Kuwait, and Oman. Two included delay periods for incubation and recovery within the SIR model produces a sensible and more accurate representation of the real time data. The reproductive number 𝑅0 is defined for the model for values of recovery time delay 𝜏2 of the infective case.ResultsIncorporating two delay periods that corresponds to the duration of the incubational and recovery periods measured for COVID-19 gives a more accurate prediction of the peak pandemic infections per geographical area. The parameter variations in the model 𝛽,𝛾,𝛼,𝜏1,𝑎𝑛𝑑 𝜏2 makeup different cases corresponding to different situations. The variations are estimated a priori based on what is being observed and collected data of an infected region to give officials better guidelines on what health policies should be enacted in the future.2 of 15ConclusionsThe empirical data provided by WHO show that the proposed new SIR model gives a better more accurate prediction of COVID-19 pandemic spreading curve. The model is shown to closely fit real time data for four countries. Simulation results are consistent with data and generated curves are well constrained. The parameters can be varied and adjusted for producing and/or reproduction of numbers within the range of each country

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Design, Analysis, and Simulation of Engineering Systems Over the World Wide Web for Educational Use

Volume 1: 22nd Computers and Information in Engineering Conference ◽

10.1115/detc2002/cie-34410 ◽

2002 ◽

Cited By ~ 2

Author(s):

P. S. Shiakolas ◽

J. Kebrle ◽

V. Chandra ◽

David Wilhite

Keyword(s):

World Wide Web ◽

Real Time ◽

World Wide ◽

Linear Time ◽

Web Browser ◽

Real Time Simulation ◽

Linear Time Invariant ◽

Time Simulation ◽

The World ◽

Time Invariant

This paper presents the use of developed software tools for engineering education accessible via a web browser over the World Wide Web. The primary purpose of this work aims at improving the understanding of engineering fundamentals through interactive real-time simulation. The environment consists of modules for interactive real-time simulation of linear time invariant dynamic systems, and for the synthesis and analysis of planar mechanisms. The user interface enables easy understanding of the required inputs from the user. The outputs are presented in pictorial, graphical and textual forms. In addition to these forms, for the mechanism modules the user has the option to view an animation of the synthesized mechanism. This work demonstrates the ease of implementation, advantages of using this technology to aid in classroom instruction and provides valuable analysis tools to engineering professionals. Examples demonstrating features of the developed tools are presented. Users with Internet access can use the developed modules at http://zodhia.uta.edu/development.

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Epidemic Spread Modeling with Time Variant Infective Population Using Pushdown Cellular Automata

Journal of Computational Environmental Sciences ◽

10.1155/2014/769064 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10

Author(s):

Senthil Athithan ◽

Vidya Prasad Shukla ◽

Sangappa Ramachandra Biradar

Keyword(s):

Cellular Automata ◽

Real Time ◽

Disease Spread ◽

Abu Dhabi ◽

Epidemic Spread ◽

Time Data ◽

Cellular Automata Model ◽

Spread Model ◽

The World ◽

Real Time Data

The world without a disease is a dream of any human being. The disease spread if not controlled could cause an epidemic situation to spread and lead to pandemic. To control an epidemic we need to understand the nature of its spread and the epidemic spread model helps us in achieving this. Here we propose an epidemic spread model which considers not only the current infective population around the population but also the infective population which remain from the previous generations for computing the next generation infected individuals. A pushdown cellular automata model which is an enhanced version of cellular automata by adding a stack component is being used to model the epidemic spread and the model is validated by the real time data of H1N1 epidemic in Abu Dhabi.

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