Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

The Application of Gillespie Algorithm in Spreading

Authors
Xiaomin Deng, Xiaomeng Wang
Corresponding Author
Xiaomeng Wang
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.110How to use a DOI?
Keywords
contagion model; epidemic; discrete-time; continuous-time; Gillespie Algorithm.
Abstract
The contagion models of disease-spread which predict the epidemics grow with time goes by have been widely researched in social networks. The discrete-time simulation method, Monte Carlo Simulation where time is discretized into uniform steps and transition rates between states are replaced by transition probabilities, are mostly applied when simulating the models. In this paper, we propose a continuous-time approach, the Gillespie algorithm, which can be used for fast simulation of stochastic processes, is event-driven rather than using equally-spaced time steps. We show how the method can be adapted to the epidemic models, mainly in the susceptible-infected model and susceptible-infected-susceptible model, and confirm the accuracy of the method with numerical simulations. Based on the accuracy of the method, we make some changes in epidemic models to make the models more applicable.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.110How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiaomin Deng
AU  - Xiaomeng Wang
PY  - 2019/04
DA  - 2019/04
TI  - The Application of Gillespie Algorithm in Spreading
BT  - 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.110
DO  - https://doi.org/10.2991/icmeit-19.2019.110
ID  - Deng2019/04
ER  -