Nathaniel Hupert1, Alvin I Mushlin, Mark A Callahan. 1. Departments of Public Health and Medicine, Weill Medical College of Cornell University, New York Presbyterian Hospital, New York City, USA.
Abstract
BACKGROUND: Post-exposure prophylaxis is a critical component of the public health response to bioterrorism. Computer simulation modeling may assist in designing antibiotic distribution centers for this task. METHODS: The authors used discrete event simulation modeling to determine staffing levels for entry screening, triage, medical evaluation, and drug dispensing stations in a hypothetical antibiotic distribution center operating in low, medium, and high disease prevalence bioterrorism response scenarios. Patient arrival rates and processing times were based on prior mass prophylaxis campaigns. Multiple sensitivity analyses examined the relationship between average staff utilization rate (UR) (i.e., percentage of time occupied in patient contact) and capacity of the model to handle surge arrivals. RESULTS: Distribution center operation required from 93 staff for the low-prevalence scenario to 111 staff for the high-prevalence scenario to process approximately 1000 people per hour within the baseline model assumptions. Excess capacity to process surge arrivals approximated (1-UR) for triage staffing. CONCLUSIONS: Discrete event simulation modeling is a useful tool in developing the public health infrastructure for bioterrorism response. Live exercises to validate the assumptions and outcomes presented here may improve preparedness to respond to bioterrorism.
BACKGROUND: Post-exposure prophylaxis is a critical component of the public health response to bioterrorism. Computer simulation modeling may assist in designing antibiotic distribution centers for this task. METHODS: The authors used discrete event simulation modeling to determine staffing levels for entry screening, triage, medical evaluation, and drug dispensing stations in a hypothetical antibiotic distribution center operating in low, medium, and high disease prevalence bioterrorism response scenarios. Patient arrival rates and processing times were based on prior mass prophylaxis campaigns. Multiple sensitivity analyses examined the relationship between average staff utilization rate (UR) (i.e., percentage of time occupied in patient contact) and capacity of the model to handle surge arrivals. RESULTS: Distribution center operation required from 93 staff for the low-prevalence scenario to 111 staff for the high-prevalence scenario to process approximately 1000 people per hour within the baseline model assumptions. Excess capacity to process surge arrivals approximated (1-UR) for triage staffing. CONCLUSIONS: Discrete event simulation modeling is a useful tool in developing the public health infrastructure for bioterrorism response. Live exercises to validate the assumptions and outcomes presented here may improve preparedness to respond to bioterrorism.
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