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Network-Based Modeling in Epidemiology: An Emphasis on Dynamics

Network-Based Modeling in Epidemiology: An Emphasis on Dynamics

Erick Stattner, Martine Collard, Nicolas Vidot
Copyright: © 2012 |Volume: 3 |Issue: 3 |Pages: 20
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781466612716|DOI: 10.4018/jismd.2012070103
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MLA

Stattner, Erick, et al. "Network-Based Modeling in Epidemiology: An Emphasis on Dynamics." IJISMD vol.3, no.3 2012: pp.46-65. http://doi.org/10.4018/jismd.2012070103

APA

Stattner, E., Collard, M., & Vidot, N. (2012). Network-Based Modeling in Epidemiology: An Emphasis on Dynamics. International Journal of Information System Modeling and Design (IJISMD), 3(3), 46-65. http://doi.org/10.4018/jismd.2012070103

Chicago

Stattner, Erick, Martine Collard, and Nicolas Vidot. "Network-Based Modeling in Epidemiology: An Emphasis on Dynamics," International Journal of Information System Modeling and Design (IJISMD) 3, no.3: 46-65. http://doi.org/10.4018/jismd.2012070103

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Abstract

The social behavior of individuals is an important factor of the transmission and the evolution of many diseases. As such, epidemic studies have attempted to integrate social aspects in dissemination modeling. Since the pioneering works of Klovdahl on AIDS in 1985, epidemiological investigations and interventions increasingly focus on social networks. Significant factors of the transmission and outbreak of many infectious diseases are the structure and nature of human interactions. Network-based modeling approaches have found various applications in epidemiology as a simple yet efficient way to represent the complexity of human relationships implicated in dissemination processes. However, most results have been obtained by considering social networks as steady stage structures. Evolving networks have not been explored. The objective is first to give an overview of network-based modeling attempts in epidemiology to analyze and understand the dissemination processes with an emphasis on dynamic networks. The authors approach is designed to understand the impact of social links dynamics on epidemic spread. The authors present the results obtained by combining network evolution patterns (link creation and deletion) and a typical epidemic model. The speed of link dynamics and the infection time strongly influence the occurrence and value of the epidemic peak.

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