SIS-SEIQR adaptive network model for pandemic influenza
This paper aims to present an SIS-SEIQR network model for pandemic influenza. We propose a network algorithm to generate an adaptive social network with dynamic hub nodes to capture the disease transmission in a human community. Effects of visiting probability on the spread of the disease are invest...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/9026 |
| _version_ | 1848745831028490240 |
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| author | Jumpen, W. Orankitjaroen, S. Boonkrong, P. Wattananon, B. Wiwatanapataphee, Benchawan |
| author_facet | Jumpen, W. Orankitjaroen, S. Boonkrong, P. Wattananon, B. Wiwatanapataphee, Benchawan |
| author_sort | Jumpen, W. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper aims to present an SIS-SEIQR network model for pandemic influenza. We propose a network algorithm to generate an adaptive social network with dynamic hub nodes to capture the disease transmission in a human community. Effects of visiting probability on the spread of the disease are investigated. The results indicate that high visiting probability increases the transmission rate of the disease. |
| first_indexed | 2025-11-14T06:23:36Z |
| format | Conference Paper |
| id | curtin-20.500.11937-9026 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:23:36Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-90262017-01-30T11:10:03Z SIS-SEIQR adaptive network model for pandemic influenza Jumpen, W. Orankitjaroen, S. Boonkrong, P. Wattananon, B. Wiwatanapataphee, Benchawan This paper aims to present an SIS-SEIQR network model for pandemic influenza. We propose a network algorithm to generate an adaptive social network with dynamic hub nodes to capture the disease transmission in a human community. Effects of visiting probability on the spread of the disease are investigated. The results indicate that high visiting probability increases the transmission rate of the disease. 2011 Conference Paper http://hdl.handle.net/20.500.11937/9026 restricted |
| spellingShingle | Jumpen, W. Orankitjaroen, S. Boonkrong, P. Wattananon, B. Wiwatanapataphee, Benchawan SIS-SEIQR adaptive network model for pandemic influenza |
| title | SIS-SEIQR adaptive network model for pandemic influenza |
| title_full | SIS-SEIQR adaptive network model for pandemic influenza |
| title_fullStr | SIS-SEIQR adaptive network model for pandemic influenza |
| title_full_unstemmed | SIS-SEIQR adaptive network model for pandemic influenza |
| title_short | SIS-SEIQR adaptive network model for pandemic influenza |
| title_sort | sis-seiqr adaptive network model for pandemic influenza |
| url | http://hdl.handle.net/20.500.11937/9026 |