Purpose: Disaster response planning is made more robust when discrete-event model simulation is employed for validation. Many features of disaster management deal with medical issues and public health. Absent any validation, response plans for disaster management are vulnerable to erroneous assumptions, mismatched resource allocations and ill focused attention.

The Center for Disease Control mandates a plan to ensure that everyone in the region be medicated within 48 hours should pathogenic agent threats be realized, detected and medication stores released as a countermeasure. The local county health department is responsible for the design and execution of the plan. Residents and visitors without exception are to be covered by the plan. The same mandate is given nationwide and diverse strategies have been employed. The need exists to evaluate the adequacy of any given proposed methodology for widespread medication in such a short interval.

Method: A case study is presented illustrating the results of evaluating disaster response plans of central Florida with discrete-event model simulation. Simulation provides the ability to evaluate the response design for a societal event that has not yet been a reality. Evaluation of the plan is directed by the discipline of embodying the plan as a software program per simulation modeling. The adaptive results are then reviewed for quantitative and qualitative implications to plan effectiveness and feasible execution success.

Results: In this case study, presumptions about resources required and layout of service were endorsed or corrected, including a relaxation of demand, excision of one step proven unnecessary and a change in bottleneck location accounting for throughput. Additional justification for specific concerns were discovered--e.g., training requirements to ensure adequate instruction without excess and uniformity of instruction as a device at simple stations to meet throughput goals under practical constraints. Achievement of throughput objective was proven feasible with adjustments made.

Conclusions: The method presented is robust and well suited for discovery, validation, and adaptation under such circumstances where issues, constraints and realizations are not friendly to monotypic analytical methods.