No abstract

No abstract

No abstract

We extend the classical SIR epidemic spread model by introducing the "quarantined" compartment. We solve (numerically) the differential equations that govern the extended model and quantify how quarantining "flattens the curve" for the proportion of infected population over time. Furthermore, we explore the potential of using drones to deliver tests, enabling mass-testing for the infection; we give a method to estimate the drone fleet needed to deliver the tests in a metropolitan area. Application of our models to COVID-19 spread in Sweden shows how the proposed methods could substantially decrease the peak number of infected people, almost without increasing the duration of the epidemic.

This paper uses a threshold based mathematical definition to estimate capacity for future sUAS traffic in low altitude uncontrolled airspace based on safety and performance considerations. It is motivated by the need to assess the impact of large-scale close proximity unmanned aircraft operations on communities and existing manned airspace. We simulate unmanned traffic over urban areas and estimate metrics focused on safety and performance efficiency. The effect of increasing traffic density on the metrics shows that safety is potentially the most critical capacity determining factor of the two.