TY - JOUR
T1 - Virtual epidemic in a virtual city
T2 - simulating the spread of influenza in a US metropolitan area
AU - Lee, Bruce Y.
AU - Bedford, Virginia L.
AU - Roberts, Mark S.
AU - Carley, Kathleen M.
N1 - Funding Information:
Supported by Grant KL2 RR024154-02 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) and NIH Roadmap for Medical Research, and its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. Information on NCRR is available at http://www.ncrr.nih.gov/ . Information on Re-engineering the Clinical Research Enterprise can be obtained from http://nihroadmap.nih.gov/clinicalresearch/overview-translational.asp . BioWar development was supported in part by Contract 290-00-0009 for Scalable BioSurveillance Technology from the Defense Advanced Research Projects Agency (DARPA) Bio-Alirt Program for work on Scalable BioSurveillance Systems, National Science Foundation (NSF) Grant IGERT9972762 to the Carnegie Mellon Center for Computational Analysis of Social and Organizational Systems (CASOS), Cooperative Agreements Number U90/CCU318753-01 and UP0/CCU318753-02 from the Centers for Disease Control and Prevention (CDC), the MacArthur Foundation, the Agency for Healthcare Research and Quality, and by the Carnegie Mellon Center on Computational Analysis of Social and Organizational Systems. Any opinions, findings, conclusions, or recommendations expressed in this report are those of the authors and do not necessarily reflect the views of DARPA, the National Science Foundation, the CDC, the MacArthur Foundation, the Agency for Healthcare Research and Quality, or the U.S. Government. Computations were performed on a Cray XD1 at the Arctic Region Supercomputing Center (ARSC) at the University of Alaska Fairbanks (UAF). Special appreciation to Neal Altman and Eric Malloy of Carnegie Mellon University, Don Bahls of ARSC, and Joyce Chang of the University of Pittsburgh.
PY - 2008/6
Y1 - 2008/6
N2 - A wide variety of biologic, physiologic, social, economic, and geographic factors may affect the transmission, spread, and impact of influenza. Recent concerns about an impending influenza epidemic have generated a need for predictive computer simulation models to forecast the spread of influenza and the effectiveness of prevention and control strategies. We designed an agent-based computer simulation of a theoretical influenza epidemic in Norfolk, Va, that included extensive city-level details and computer representations of every Norfolk citizen, including their expected behavior and social interactions. The simulation introduced 200 infected cases on November 27, 2002 (day 87), and tracked the progress of the epidemic. On average, the prevalence peaked on day 178 (12.2% of the population). Our model showed a cyclical variation in influenza cases by day of the week with fewer people being exposed on weekends, differences in emergency room and clinic visits by day of the week, an earlier peak in influenza cases, and persistent high prevalence among people age 65 or older and the daily prevalence of infection among health-care workers. The level of detail included in our simulation model made these findings possible. Compared with other existing models, our model has a very extensive and detailed social network, which may be important because individuals with more social interactions and extensive social networks may be more likely to spread influenza. Our simulation may serve as a virtual laboratory to better understand the way different factors and interventions affect the spread of influenza.
AB - A wide variety of biologic, physiologic, social, economic, and geographic factors may affect the transmission, spread, and impact of influenza. Recent concerns about an impending influenza epidemic have generated a need for predictive computer simulation models to forecast the spread of influenza and the effectiveness of prevention and control strategies. We designed an agent-based computer simulation of a theoretical influenza epidemic in Norfolk, Va, that included extensive city-level details and computer representations of every Norfolk citizen, including their expected behavior and social interactions. The simulation introduced 200 infected cases on November 27, 2002 (day 87), and tracked the progress of the epidemic. On average, the prevalence peaked on day 178 (12.2% of the population). Our model showed a cyclical variation in influenza cases by day of the week with fewer people being exposed on weekends, differences in emergency room and clinic visits by day of the week, an earlier peak in influenza cases, and persistent high prevalence among people age 65 or older and the daily prevalence of infection among health-care workers. The level of detail included in our simulation model made these findings possible. Compared with other existing models, our model has a very extensive and detailed social network, which may be important because individuals with more social interactions and extensive social networks may be more likely to spread influenza. Our simulation may serve as a virtual laboratory to better understand the way different factors and interventions affect the spread of influenza.
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U2 - 10.1016/j.trsl.2008.02.004
DO - 10.1016/j.trsl.2008.02.004
M3 - Article
C2 - 18514138
AN - SCOPUS:44449174434
SN - 1931-5244
VL - 151
SP - 275
EP - 287
JO - Translational Research
JF - Translational Research
IS - 6
ER -