title: A sequential experimental design method to evaluate a combination of school closure and vaccination policies to control an H1N1-like pandemic.
abstract: BACKGROUND
  During the 2009 H1N1 pandemic, computational agent-based models (ABMs) were extensively used to evaluate interventions to control the spread of emerging pathogens. However, evaluating different possible combinations of interventions using ABMs can be computationally very expensive and time-consuming. Therefore, most policy studies have examined the impact of a single policy decision.
  
  
  OBJECTIVE
  To apply a sequential experimental design method with an ABM to analyze policy alternatives composed of a combination of school closure and vaccination policies to provide a set of promising "optimal" combinations of policies to control an H1N1-type epidemic to policy makers.
  
  
  METHODS
  We used an open-source agent-based modeling system, FRED (A Framework for Reconstructing Epidemiological Dynamic), to simulate the spread of an H1N1 epidemic in Alleghany County, Pennsylvania, with a census-based synthetic population. We used an approach called best subset selection method to evaluate 72 alternative policies consisting of a combination of options for school closure threshold, closure duration, Advisory Committee on Immunization Practices prioritization, and second-dose vaccination prioritization policies. Using the attack rate as a performance measure, best subset selection enabled us to eliminate inferior alternatives and identify a small group of alternative policies that could be further evaluated on the basis of other criteria.
  
  
  RESULTS
  Our sequential design approach to evaluate a combination of alternative mitigation policies leads to a savings in computational effort by a factor of 2 when examining combinations of school closure and vaccination policies.
  
  
  CONCLUSIONS
  Best subset selection demonstrates a substantial reduction in the computational burden of a large-scale ABM in evaluating several alternative policies. Our method also provides policy makers with a set of promising policy combinations for further evaluation based on implementation considerations or other criteria.
authors.affiliation: Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA. lol11@pitt.edu
authors.forename: Kiatikun Louis
authors.initials: KL
authors.lastname: Luangkesorn
authors.forename: Farhad
authors.initials: F
authors.lastname: Ghiasabadi
authors.forename: Jagpreet
authors.initials: J
authors.lastname: Chhatwal
mesh_terms: D006291:Health Policy; D006801:Humans; D017589:Immunization Programs; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000517:prevention & control*; D032541:Mass Vaccination; D008962:Models, Theoretical; D058873:Pandemics / Q000517:prevention & control*; D010414:Pennsylvania; D012574:Schools
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