Epidemics of novel or re-emerging infectious diseases have quickly spread globally via air travel, as highlighted by pandemic H1N1 influenza in 2009 (pH1N1). Federal, state, and local public health responders must be able to plan for and respond to these events at aviation points of entry. The emergence of a novel influenza virus and its spread to the United States were simulated for February 2009 from 55 international metropolitan areas using three basic reproduction numbers (R(0)): 1.53, 1.70, and 1.90. Empirical data from the pH1N1 virus were used to validate our SEIR model. Time to entry to the U.S. during the early stages of a prototypical novel communicable disease was predicted based on the aviation network patterns and the epidemiology of the disease. For example, approximately 96% of origins (R(0) of 1.53) propagated a disease into the U.S. in under 75 days, 90% of these origins propagated a disease in under 50 days. An R(0) of 1.53 reproduced the pH1NI observations. The ability to anticipate the rate and location of disease introduction into the U.S. provides greater opportunity to plan responses based on the scenario as it is unfolding. This simulation tool can aid public health officials to assess risk and leverage resources efficiently.

In an earlier paper, an existing Air and Space Operations Center (AOC) process model (i.e., Petri net) and new global and mission models for the environment in which the AOC operates (i.e., System Dynamics) were linked (federated). The focus of this paper is the development of an operator‐environment model (i.e., Agent‐Based Model). An existing systems framework for attention allocation of operators within the AOC has been implemented that supports multiple modeling paradigms. The results for linking the Petri net and System Dynamics models are summarized, and new results for the Agent‐Based Model are presented based on a pilot‐down scenario. It has been observed that many AOC operators can become distracted by a pilot‐down critical event, even if the operator is not able to directly assist in the rescue. Furthermore, this distraction has been hypothesized to have a detrimental effect on the activities the non‐involved operators are currently handling.

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Wider adoption of Lean‐Agile software development methods has prompted interest in tailoring these small team methods to meet the needs of larger organizations. The Scaled Agile Framework (SAFe) for Lean Software Development incorporates many Lean‐Agile best practices and has become a leading proposition for applying agile methods at scale (Leffingwell 2016). To help managers visualize how SAFe might work for their organization, we have created a management flight simulator based on a system dynamics model of SAFe. Using these tools, decision makers who consider transitioning their organizations to SAFe can perform virtual experiments by changing parameters such as team size, developer experience level, sprint length, and whether developers perform continuous integration to explore how these programmatic decisions affect the rate at which work is completed. Such process prototyping can provide a justification for adopting SAFe, which often includes costly training and organizational restructuring, by forming a basis for quantifying return on investment. We applied our system dynamics model to a real program and showed that instituting continuous integration can improve throughput by 54% while decreasing defects by 88%.