Viral Economics: An Epidemiological Model of Knowledge Diffusion in Economics

2011 ◽  
Author(s):  
Nicole Gurley ◽  
Daniel K. N. Johnson
Keyword(s):  
Knowledge Diffusion ◽  
Epidemiological Model

Viral economics: an epidemiological model of knowledge diffusion in economics

2016 ◽  
Vol 69 (1) ◽  
pp. 320-331 ◽  
Author(s):  
Nicole Gurley ◽  
Daniel K.N. Johnson
Keyword(s):  
Knowledge Diffusion ◽  
Epidemiological Model

scholarly journals The SARS-CoV-2 pandemic course in Saudi Arabia: A dynamic epidemiological model

2020 ◽  
Vol 5 ◽  
pp. 766-771 ◽  
Author(s):  
Abdullah Murhaf Al-Khani ◽  
Mohamed Abdelghafour Khalifa ◽  
Abdulrahman Almazrou ◽  
Nazmus Saquib
Keyword(s):  
Saudi Arabia ◽  
Epidemiological Model

scholarly journals Epidemiological modeling in StochSS Live!

2021 ◽  
Author(s):  
Richard Jiang ◽  
Bruno Jacob ◽  
Matthew Geiger ◽  
Sean Matthew ◽  
Bryan Rumsey ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Epidemiological Model ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Based ◽  
Epidemiological Modeling ◽  
Modeling Simulation ◽  
Wide Range ◽  
Biochemical Systems

Abstract Summary We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results. Availability and implementation StochSS Live! is freely available at https://live.stochss.org/ Supplementary information Supplementary data are available at Bioinformatics online.

A probabilistic epidemiological model

2021 ◽  
Vol 16 (1) ◽  
pp. 15-23
Author(s):  
Hal M. Switkay
Keyword(s):  
United States ◽  
United Kingdom ◽  
United States Of America ◽  
Compartmental Model ◽  
The United States ◽  
Epidemiological Model ◽  
Probabilistic Methods ◽  
Infinite Intervals ◽  
Best Fit ◽  
Family Of Distributions

We construct a model for the progress of the 2020 coronavirus epidemic in the United States of America, using probabilistic methods rather than the traditional compartmental model. We employ the generalized beta family of distributions, including those supported on bounded intervals and those supported on semi-infinite intervals. We compare the best-fit distributions for daily new cases and daily new deaths in America to the corresponding distributions for United Kingdom, Spain, and Italy. We explore how such a model might be justified theoretically in comparison to the apparently more natural compartmental model. We compare forecasts based on these models to observations, and find the forecasts useful in predicting total pandemic deaths.

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