Applications of the Delay Stochastic Simulation Algorithm (DSSA) in Mathematical Epidemiology

The calculation of the probability of a minor outbreak is crucial in analyzing a stochastic epidemic model. For stochastic epidemic models with fixed delays, the linear chain trick is applied to transform the delayed models into a family of ODE models with increasing shape parameters. We then prove...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Mathematics (Basel) Ročník 10; číslo 20; s. 3759
Hlavní autor: Bai, Fan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2022
Témata:
Algorithms
branching process
Computer simulation
delay differential equations
Epidemics
Epidemiology
Food science
Gene expression
Infections
Infectious diseases
Learning models (Stochastic processes)
Markov analysis
Markov chain
Markov chains
Mathematical models
numerical methods
Outbreaks
Population
Probability
probability of a minor epidemic outbreak
Probability theory
Random variables
Signal transduction
Simulation
stochastic epidemic models with delays
Stochastic models
Viral infections
United States
ISSN:2227-7390, 2227-7390
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The calculation of the probability of a minor outbreak is crucial in analyzing a stochastic epidemic model. For stochastic epidemic models with fixed delays, the linear chain trick is applied to transform the delayed models into a family of ODE models with increasing shape parameters. We then prove that the well-established results on the probability of a minor outbreak for continuous-time Markov chain (CTMC) epidemic models also hold for the stochastic epidemic models with fixed delays. All theoretical results are verified by numerical simulations implemented by the delay stochastic simulation algorithm (DSSA) in Python. It is shown that DSSA is able to generate exact realizations for underlying delayed models in the context of mathematical epidemiology, and therefore, provides insights into the effect of delays during the outbreak phases of epidemics.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-7390
2227-7390
DOI:10.3390/math10203759