A closed-form equation, the Fizzle Equation, was derived from a mathematical model predicting Severe Acute Respiratory Virus-2 dynamics, optimized for a 4000-student university cohort. This equation sought to determine the frequency and percentage of random surveillance testing required to prevent an outbreak, enabling an institution to develop scientifically sound public health policies to bring the effective reproduction number of the virus below one, halting virus progression. Model permutations evaluated the potential spread of the virus based on the level of random surveillance testing, increased viral infectivity and implementing additional safety measures. The model outcomes included: required level of surveillance testing, the number of infected individuals, and the number of quarantined individuals. Using the derived equations, this study illustrates expected infection load and how testing policy can prevent outbreaks in an institution. Furthermore, this process is iterative, making it possible to develop responsive policies scaling the amount of surveillance testing based on prior testing results, further conserving resources.

Army collective SBT uses physically simulated military equipment (PSME) or virtual military equipment (VME) • PSME approaches, such as CCTT and AVCATT, attempt to replicate the physical environment, controls, and sensory cues of an actual tank or aircraft. PSME development, use, and maintenance are expensive, and soldiers’ access to PSME equipment may be limited. • VME uses personal computers and other equipment in ways similar to commercial first-person-shooter games. These have less physical realism but are easier and less expensive to operate, maintain, and upgrade and are more accessible to soldiers, allowing for more practice.

The U.S. Army uses various virtual systems to help soldiers learn, practice, and demonstrate proficiency in collective skills. This report examines the fidelity of virtual systems to train U.S. Army platoon- and company-level collective skills and estimates the costs and effectiveness of using simulators with different degrees of fidelity for collective training.