TY - JOUR
T1 - Disease dynamics in a dynamic social network
AU - Christensen, Claire
AU - Albert, István
AU - Grenfell, Bryan
AU - Albert, Réka
N1 - Funding Information:
The authors would like to thank Andrew Conlan for helpful discussions and RTI International for computing time on the MIDAS cluster. This work was supported by an NSF IGERT Fellowship, Consortium for Education in Many-body Applications to CC, NSF grants CCF-0643529 and EF-0742373 to RA and a Gates Foundation grant to BG. BG was supported by the RAPIDD Program of the Science and Technology Directorate, US Department of Homeland Security and the Fogarty International Center, NIH; NSF grant EF-0742373 and NIH grant R01 GM083983-01 .
PY - 2010/7/1
Y1 - 2010/7/1
N2 - We develop a framework for simulating a realistic, evolving social network (a city) into which a disease is introduced. We compare our results to prevaccine era measles data for England and Wales, and find that they capture the quantitative and qualitative features of epidemics in populations spanning two orders of magnitude. Our results provide unique insight into how and why the social topology of the contact network influences the propagation of the disease through the population. We argue that network simulation is suitable for concurrently probing contact network dynamics and disease dynamics in ways that prior modeling approaches cannot and it can be extended to the study of less well-documented diseases.
AB - We develop a framework for simulating a realistic, evolving social network (a city) into which a disease is introduced. We compare our results to prevaccine era measles data for England and Wales, and find that they capture the quantitative and qualitative features of epidemics in populations spanning two orders of magnitude. Our results provide unique insight into how and why the social topology of the contact network influences the propagation of the disease through the population. We argue that network simulation is suitable for concurrently probing contact network dynamics and disease dynamics in ways that prior modeling approaches cannot and it can be extended to the study of less well-documented diseases.
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U2 - 10.1016/j.physa.2010.02.034
DO - 10.1016/j.physa.2010.02.034
M3 - Article
C2 - 20563303
AN - SCOPUS:77950930008
SN - 0378-4371
VL - 389
SP - 2663
EP - 2674
JO - Physica A: Statistical Mechanics and its Applications
JF - Physica A: Statistical Mechanics and its Applications
IS - 13
ER -
