Utilization of process models for laboratory capacity planning

Historical data suggest that a future influenza pandemic is certain. To mitigate the effects of a pandemic, state public health laboratories maintain influenza diagnostic and surveillance testing capabilities to monitor virus changes, trigger mitigation strategies, generate national surveillance data, and measure mitigation effectiveness. By understanding the capacity gaps and resource constraints involved in influenza testing, state laboratories can develop pandemic plans to increase their pandemic testing capacity. To forecast influenza testing capacity over the course of a pandemic, we developed an Extend™ discrete event process model for state laboratories. The model incorporates each major step involved in the RT-PCR testing process. We collected data from 20 state and city public health laboratories and modeled the maximum baseline and pandemic capacity for each laboratory. From these experiments, we determined that the average pandemic workload exceeded the average maximum baseline capacity by 9 fold for the 20 laboratories. Analysis of pandemic scenarios also indicated significant potential shortfalls in laboratory staff for 85% of laboratories, extraction platforms for 50% of laboratories, and support staff for 35% of the laboratories. Due to the magnitude of laboratory staff and equipment required to augment laboratory capacity during a pandemic, we propose multiple strategies for increasing laboratory capacity that includes reducing the number of specimens that require testing, changing testing procedures, and increasing testing resources.