Uncertainty and operational considerations in mass prophylaxis workforce planning.
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.
Hupert
Nathaniel
N
Department of Public Health, 402 E 67th St, New York, NY 10065, USA. nah2005@med.cornell.edu
Xiong
Wei
W
King
Kathleen
K
Castorena
Michelle
M
Hawkins
Caitlin
C
Wu
Cindie
C
Muckstadt
John A
JA
D000881:Anthrax / Q000188:drug therapy; D000900:Anti-Bacterial Agents / Q000600:supply & distribution; D000998:Antiviral Agents / Q000600:supply & distribution*; D004189:Disaster Planning / Q000458:organization & administration*; D004196:Disease Outbreaks; D006282:Health Personnel / Q000458:organization & administration*; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D009010:Monte Carlo Method; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration; D011635:Public Health Administration / Q000379:methods; D035501:Uncertainty; D014850:Waiting Lists
D000900:Anti-Bacterial Agents; D000998:Antiviral Agents