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.
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.
Authors: Bruce Y Lee; Shawn T Brown; George W Korch; Philip C Cooley; Richard K Zimmerman; William D Wheaton; Shanta M Zimmer; John J Grefenstette; Rachel R Bailey; Tina-Marie Assi; Donald S Burke Journal: Vaccine Date: 2010-05-16 Impact factor: 3.641
Authors: Bruce Y Lee; Shawn T Brown; Rachel R Bailey; Richard K Zimmerman; Margaret A Potter; Sarah M McGlone; Philip C Cooley; John J Grefenstette; Shanta M Zimmer; William D Wheaton; Sandra Crouse Quinn; Ronald E Voorhees; Donald S Burke Journal: Health Aff (Millwood) Date: 2011-06 Impact factor: 6.301
Authors: Bruce Y Lee; Shawn T Brown; Philip Cooley; John J Grefenstette; Richard K Zimmerman; Shanta M Zimmer; Margaret A Potter; Roni Rosenfeld; William D Wheaton; Ann E Wiringa; Kristina M Bacon; Donald S Burke Journal: Am J Prev Med Date: 2010-11 Impact factor: 5.043
Authors: Bruce Y Lee; Shawn T Brown; Philip C Cooley; Richard K Zimmerman; William D Wheaton; Shanta M Zimmer; John J Grefenstette; Tina-Marie Assi; Timothy J Furphy; Diane K Wagener; Donald S Burke Journal: Am J Prev Med Date: 2009-12-30 Impact factor: 5.043
Authors: Bruce Y Lee; Shawn T Brown; Philip Cooley; Maggie A Potter; William D Wheaton; Ronald E Voorhees; Samuel Stebbins; John J Grefenstette; Shanta M Zimmer; Richard K Zimmerman; Tina-Marie Assi; Rachel R Bailey; Diane K Wagener; Donald S Burke Journal: J Public Health Manag Pract Date: 2010 May-Jun
Authors: Shawn T Brown; Julie H Y Tai; Rachel R Bailey; Philip C Cooley; William D Wheaton; Margaret A Potter; Ronald E Voorhees; Megan LeJeune; John J Grefenstette; Donald S Burke; Sarah M McGlone; Bruce Y Lee Journal: BMC Public Health Date: 2011-05-20 Impact factor: 3.295