A sequential experimental design method to evaluate a combination of school closure and vaccination policies to control an H1N1-like pandemic.

TitleA sequential experimental design method to evaluate a combination of school closure and vaccination policies to control an H1N1-like pandemic.
Publication TypeJournal Article
Year of Publication2013
AuthorsLuangkesorn KLouis, Ghiasabadi F, Chhatwal J
JournalJ Public Health Manag Pract
Volume19 Suppl 2
PaginationS37-41
Date Published2013 Sep-Oct
ISSN1550-5022
KeywordsHealth Policy, Humans, Immunization Programs, Influenza A Virus, H1N1 Subtype, Influenza, Human, Mass Vaccination, Models, Theoretical, Pandemics, Pennsylvania, Schools
Abstract

CONTEXT: 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.

DOI10.1097/PHH.0b013e3182939a5c
Alternate JournalJ Public Health Manag Pract
PubMed ID23903393
PubMed Central IDPMC3829782
Grant List5U54GM088491-03 / GM / NIGMS NIH HHS / United States
U54 GM088491 / GM / NIGMS NIH HHS / United States
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