scholarly journals Stochastic extinction and persistence of a parasite–host epidemiological model

2016 ◽  
Vol 462 ◽  
pp. 586-602 ◽  
Author(s):  
Yuting Liu ◽  
Meijing Shan ◽  
Xinze Lian ◽  
Weiming Wang
Keyword(s):  
Epidemiological Model ◽  
Stochastic Extinction

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.

scholarly journals Backward bifurcation and oscillations in a nested immuno-eco-epidemiological model

2017 ◽  
Vol 12 (1) ◽  
pp. 51-88 ◽  
Author(s):  
Michael Barfield ◽  
Maia Martcheva ◽  
Necibe Tuncer ◽  
Robert D. Holt
Keyword(s):  
Backward Bifurcation ◽  
Epidemiological Model

scholarly journals Stochasticity and heterogeneity in host–vector models

2007 ◽  
Vol 4 (16) ◽  
pp. 851-863 ◽  
Author(s):  
Alun L Lloyd ◽  
Ji Zhang ◽  
A.Morgan Root
Keyword(s):  
Branching Process ◽  
Moment Equations ◽  
Demographic Stochasticity ◽  
The Face ◽  
Process Methodology ◽  
The Impact ◽  
Malaria Model ◽  
Heterogeneous Transmission ◽  
Insight Into ◽  
Stochastic Extinction

Demographic stochasticity and heterogeneity in transmission of infection can affect the dynamics of host–vector disease systems in important ways. We discuss the use of analytic techniques to assess the impact of demographic stochasticity in both well-mixed and heterogeneous settings. Disease invasion probabilities can be calculated using branching process methodology. We review the use of this theory for host–vector infections and examine its use in the face of heterogeneous transmission. Situations in which there is a marked asymmetry in transmission between host and vector are seen to be of particular interest. For endemic infections, stochasticity leads to variation in prevalence about the endemic level. If these fluctuations are large enough, disease extinction can occur via endemic fade-out. We develop moment equations that quantify the impact of stochasticity, providing insight into the likelihood of stochastic extinction. We frame our discussion in terms of the simple Ross malaria model, but discuss extensions to more realistic host–vector models.

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