Wannika JumpenSomsak OrankitjaroenPichit BoonkrongBoonmee WattananonBenchawan WiwatanapatapheeMahidol UniversitySouth Carolina Commission on Higher Education2018-05-032018-05-032011-09-29Proceedings of the European Computing Conference, ECC '11. (2011), 147-1512-s2.0-79960106726https://repository.li.mahidol.ac.th/handle/20.500.14594/11771This 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.Mahidol UniversityComputer ScienceMathematicsConference PaperSCOPUS