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|Title: ||Dynamic Modeling of Workforce Requirements for Mass Prophylaxis Under Highly Uncertain Conditions: Cornell Dynamic POD Simulator|
|Authors: ||Castorena, Michelle|
|Keywords: ||Dynamic POD Simulator|
|Issue Date: ||26-Mar-2009|
|Series/Report no.: ||1471|
|Abstract: ||The public health response to a bioterrorist attack or other large-scale health emergency may include mass prophylaxis using multiple Points of Dispensing (PODs) to deliver countermeasures rapidly to affected populations. Although computer models exist to determine "optimal" staffing levels at PODs under certain steady-state conditions, no quantitative studies address the requirements of the POD workforce management systems needed to enable efficient, population-wide coverage in the face of a dynamic and uncertain operational environment.
Our goal was to investigate quantitatively the impact of dynamic and uncertain patient arrival patterns on workforce requirements and on overall POD effectiveness and efficiency over the duration of a mass prophylaxis campaign.
To investigate the dynamic behavior of POD systems, many extensive simulation experiments were conducted using a Monte Carlo simulation model, called the Dynamic POD Simulator (D-PODS). Using this simulation environment, we designed POD station layouts, capacities, staffing patterns, patient types, and work flows and then observed the consequences of operating PODs based on these plans under various patient arrival scenarios. The D-PODS user interface is an Excel worksheet and the model is implemented in Visual Basic.
Using several illustrative POD experiments, we demonstrate that uncertain patient arrival patterns require higher staffing levels than might be expected if a stationary environment is assumed. These experiments further show that PODs may develop severe bottlenecks unless staffing levels vary over time to meet changing patient arrival patterns. Because of the unpredictability of the operating environment, efficient POD networks require command and control systems capable of dynamically adjusting intra- and inter-POD staffing levels to meet demand. Furthermore, we show that fewer large PODs require a smaller total staff than many small PODs require to serve the same number of patients.
We conclude that 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. Our experiments demonstrate the critical role of variation and uncertainty in POD arrival patterns in establishing effective POD staffing plans. These experiments also highlight the need for command and control systems to be created to manage emergency response successfully.|
|Appears in Collections:||ORIE Technical Reports|
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