A stochastic epidemic model with G-Brownian motion

2021 ◽  
pp. 1-7
Author(s):  
Ping He ◽  
Yong Ren ◽  
Defei Zhang
Keyword(s):  
Brownian Motion ◽  
Epidemic Model ◽  
Stochastic Epidemic ◽  
Stochastic Epidemic Model

scholarly journals Modeling the dynamics of novel coronavirus (COVID-19) via stochastic epidemic model

2021 ◽  
Vol 60 (4) ◽  
pp. 4121-4130
Author(s):  
Ghulam Hussain ◽  
Tahir Khan ◽  
Amir Khan ◽  
Mustafa Inc ◽  
Gul Zaman ◽  
...  
Keyword(s):  
Epidemic Model ◽  
Stochastic Epidemic ◽  
Stochastic Epidemic Model ◽  
Novel Coronavirus

Environmental variability in a stochastic epidemic model

2018 ◽  
Vol 329 ◽  
pp. 210-226 ◽  
Author(s):  
Yongli Cai ◽  
Jianjun Jiao ◽  
Zhanji Gui ◽  
Yuting Liu ◽  
Weiming Wang
Keyword(s):  
Epidemic Model ◽  
Environmental Variability ◽  
Stochastic Epidemic ◽  
Stochastic Epidemic Model

The transition probabilities of the general stochastic epidemic model

1975 ◽  
Vol 12 (3) ◽  
pp. 415-424 ◽  
Author(s):  
Richard J. Kryscio
Keyword(s):  
Stochastic Model ◽  
Convenient Method ◽  
Epidemic Model ◽  
Transition Probabilities ◽  
Stochastic Epidemic ◽  
General Stochastic Model ◽  
Stochastic Epidemic Model ◽  
Forward Equations ◽  
General Stochastic

Recently, Billard (1973) derived a solution to the forward equations of the general stochastic model. This solution contains some recursively defined constants. In this paper we solve these forward equations along each of the paths the process can follow to absorption. A convenient method of combining the solutions for the different paths results in a simplified non-recursive expression for the transition probabilities of the process.

scholarly journals Predicting the course of meningococcal disease outbreaks in closed subpopulations

1999 ◽  
Vol 123 (3) ◽  
pp. 359-371 ◽  
Author(s):  
J. RANTA ◽  
P. H. MÄKELÄ ◽  
A. TAKALA ◽  
E. ARJAS
Keyword(s):  
Meningococcal Disease ◽  
Epidemic Model ◽  
Additional Data ◽  
Disease Outbreaks ◽  
Monte Carlo Sampling ◽  
Invasive Disease ◽  
Data Sets ◽  
Multiple Sources ◽  
Stochastic Epidemic ◽  
Stochastic Epidemic Model

A stochastic epidemic model was applied to meningococcal disease outbreaks in defined small populations such as military garrisons and schools. Meningococci are spread primarily by asymptomatic carriers and only a small proportion of those infected develop invasive disease. Bayesian predictions of numbers of invasive cases were developed, based on observed data using a stochastic epidemic model. We used additional data sets to model both disease probability and duration of carriage. Markov chain Monte Carlo sampling techniques were used to compute the full posterior distribution which summarized all information drawn together from multiple sources.

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