Abstract
This paper aims to present a local network model of Susceptible-Exposed-Infectious-Quarantined-Recovered disease transmission taking into account the community structure of the population. The population structure is generated by genetic algorithm based on the network modularity concept for the heterogeneous property. The basic reproductive number of the model is derived and used to predict the epidemiological situation. In numerical simulation, the disease transmissions within and across the communities are considered. The results show that this approach is able to capture the essential feature of epidemic spreading in human community.
| Original language | English |
|---|---|
| Pages (from-to) | 504-511 |
| Number of pages | 8 |
| Journal | International Journal of Mathematics and Computers in Simulation |
| Volume | 6 |
| Issue number | 5 |
| Publication status | Published - 2012 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Community detection
- Complex network
- Disease transmission
- Genetic algorithm
- Multi-group epidemic model
- Network modularity
- SEIQR network model
- Stability analysis
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