A simulation model for policy decision analysis: a case of pandemic influenza on a university campus
O M Araz,
T Lant,
J W Fowler and
M Jehn
Journal of Simulation, 2011, vol. 5, issue 2, 89-100
Abstract:
Pandemic influenza preparedness plans strongly focus on efficient mitigation strategies including social distancing, logistics and medical response. These strategies are formed by multiple decision makers before a pandemic outbreak and during the pandemic in local communities, states and nation-wide. In this paper, we model the spread of pandemic influenza in a local community, a university, and evaluate the mitigation policies. Since the development of an appropriate vaccine requires a significant amount of time and available antiviral quantities can only cover a relatively small proportion of the population, university decision makers will first focus on non-pharmaceutical interventions. These interventions include social distancing and isolation. The disease spread is modelled as differential equations-based compartmental model. The system is simulated for multiple non-pharmaceutical interventions such as social distancing including suspending university operations, evacuating dorms and isolation of infected individuals on campus. Although the model is built based on the preparedness plan of one of the biggest universities in the world, Arizona State University, it can easily be generalized for other colleges and universities. The policies and the decisions are tested by several simulation runs and evaluations of the mitigation strategies are presented in the paper.
Date: 2011
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1057/jos.2010.6 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tjsmxx:v:5:y:2011:i:2:p:89-100
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjsm20
DOI: 10.1057/jos.2010.6
Access Statistics for this article
Journal of Simulation is currently edited by Christine Currie
More articles in Journal of Simulation from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().