UMMS Affiliation

Department of Quantitative Health Sciences, Division of Biostatistics and Health Services Research

Publication Date


Document Type



Biostatistics | Computer Sciences | Mathematics | Medicine and Health | Public Health | Statistical Models


Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.


UMCCTS funding, Epidemic Control, Epidemic modeling, Optimal Strategies, Social Contact Network

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Copyright 2015 IEEE. Open access. Publisher PDF posted as allowed by the publisher's author rights policy at

DOI of Published Version



Zhaoyang Zhang; Honggang Wang; Chonggang Wang; Hua Fang, "Modeling Epidemics Spreading on Social Contact Networks," in Emerging Topics in Computing, IEEE Transactions on , vol.3, no.3, pp.410-419, Sept. 2015. doi: 10.1109/TETC.2015.2398353. Link to article on publisher's site

Journal/Book/Conference Title

IEEE Transactions on Emerging Topics in Computing

PubMed ID


Related Resources

Link to article on PubMed