Applications of checkpoint allocation in Covid context

Motivated by the onset of the Covid 19 pandemic, this paper studies ways to define and locate roadside control points for various time periods in an attempt to reduce infection rates in a resource-limited environment. From open data of New York City on traffic volume and Covid 19, we created a dataset to generate the points of interest and a metric to evaluate those chosen as control points. We built an integer programming model for the proposed scenario and solved it using a mathematical solver. We simulate different scenarios of conflicts between resources and points of interest, over a horizon. In a dataset of over 15000 instances, we solve 13000 in less than 2 minutes each and show hard-to-solve cases.