Background: The public health response to an influenza pandemic or other large-scale health emergency may include mass prophylaxis using multiple points of dispensing (PODs) to deliver countermeasures rapidly to affected populations Computer models created to date to determine "optimal" staffing levels at PODs typically assume stable patient demand for service The authors investigated POD function under dynamic and uncertain operational environments Methods: The authors constructed a Monte Carlo simulation model of mass prophylaxis (the Dynamic POD Simulator, or D-PODS) to assess the consequences of nonstationary patient arrival patterns on POD function under a variety of POD layouts and staffing plans Compared are the performance of a standard POD layout under steady-state and variable patient arrival rates that may mimic real-life variation in patient demand Results: To achieve similar performance, PODs functioning under nonstationary patient arrival rates require higher staffing levels than would be predicted using the assumption of stationary arrival rates Furthermore, PODs may develop severe bottlenecks unless staffing levels vary over time to meet changing patient arrival patterns. Efficient POD networks therefore require command and control systems capable of dynamically adjusting intra- and inter-POD staff levels to meet demand. In addition, under real-world operating conditions of heightened uncertainty, fewer large PODs will require a smaller total staff than many small PODs to achieve comparable performance Conclusions: Modeling environments that capture the effects of fundamental uncertainties in public health disasters are essential for the realistic evaluation of response mechanisms and policies D-PODS quantifies POD operational efficiency under more realistic conditions than have been modeled previously The authors' experiments demonstrate that effective POD staffing plans must be responsive to variation and uncertainty in POD arrival patterns These experiments highlight the need for command and control systems to be created to manage emergency response successfully (Disaster Med Public Health Preparedness 2009,3(Suppl 2) S121-S131)
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Royal Hallamshire Hosp, Dept Neurosurg, Sheffield, S Yorkshire, EnglandRoyal Hallamshire Hosp, Dept Neurosurg, Sheffield, S Yorkshire, England
Sinha, Saurabh
McKenna, Grainne
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Barts Hlth NHS Trust, Dept Neurosurg, Royal London Hosp, London, EnglandRoyal Hallamshire Hosp, Dept Neurosurg, Sheffield, S Yorkshire, England
McKenna, Grainne
Whitfield, Peter
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Derriford Hosp, South West Neurosurg Ctr, Dept Neurosurg, Plymouth, Devon, EnglandRoyal Hallamshire Hosp, Dept Neurosurg, Sheffield, S Yorkshire, England
Whitfield, Peter
Thomson, Simon
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Leeds Gen Infirm, Dept Neurosurg, G Floor,Jubilee Wing,Great George St, Leeds LS1 3EX, W Yorkshire, EnglandRoyal Hallamshire Hosp, Dept Neurosurg, Sheffield, S Yorkshire, England
Thomson, Simon
Kitchen, Neil
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Natl Hosp Neurol & Neurosurg, Queen Sq, London, EnglandRoyal Hallamshire Hosp, Dept Neurosurg, Sheffield, S Yorkshire, England