simpar (SIMulate PAndemic Response) simulates the spread of a disease through a
heterogeneous population using an SIR model.
The groups module can be used to manage a heterogeneous population comprised
of “meta-groups” with varying contact levels. The tool focuses on providing
functionality for assessing pandemic response strategies such as isolation
protocols, testing regimes (with varying tests), and vaccination requirements.
The Strategy class is used to define a potential strategy. The Scenario
class is used to manage the parameters pertaining to a scenario under which a
disease is spreading. This consists of a population, environment parameters
(e.g. outside rate of infection), and disease parameters (e.g. symptomatic
rate). Lastly, the Trajectory class offers methods to compute metrics on a
simulation of some strategy applied to a scenario. For more details,
see the Documentation.
In March 2020, I was sent home from Cornell amidst the Covid-19 pandemic. I eagerly awaited the university’s decision for returning to campus in the fall. In June, it was announced that Cornell would return to campus for a hybrid semester. This decision to return was a result of the Cornell Modeling Team’s work led by Peter Frazier. This Forbes article details how the team discovered returning to campus was the safer option. Given this decision, the university had a challenging obstacle ahead: creating a class schedule for Fall 2020.
The first hurdle in creating the class schedule was determining new room capacities subject to the six foot social distancing requirement. This posed an interesting optimization problem which an ORIE team led by Jody Zhu tackled. More information on that effort can be found in this article. In August of 2020, I was asked to join the Cornell Course Roster Scheduling team led by David Shmoys, Oktay Gunluk, David Williamson, and Brenda Dietrich. I aided in collecting course information and preferences from over 80 academic departments to be fed in to an optimization model designed by ORIE PhD student, Conner Lawless. Afterwards, I led the effort to manage department requests for changes to the fall course roster, and developed the room assignment model for exams. More about these efforts can be found in this article. The hard work of these teams led to Cornell’s ability to provide an in-person education for the Fall 2020 semester. This Bloomberg article summarizes the victory.
In Fall 2021, I was asked to join a team of Cornell ORIE faculty and PhD students to advise Cornell leadership on the university’s Covid-19 policies. I developed the cotat Python package to visualize Cornell contact tracing data. In December 2021, Cornell was the first university to see Omicron cases on campus. See this CNN article. Visualizations created by this tool were used in an internal report prepared by Jaylen C. Perkins, MPH providing insight into spread during this surge. In January of 2022, I led the development of simpar, a Python package used to simulate the spread of Covid-19 through a heterogeneous population. This tool was central in generating predictions which directly informed Cornell’s Spring 2022 policies and were used to advise administration throughout the spring semester.
cotat is a visualization tool for the analysis of contact tracing data. Given a dataframe of people (along with their attributes) and a dataframe of known contacts among the individuals, cotat exports an interactive HTML visualization of the network. Furthermore, certain columns of the people dataframe can be labeled as “membership columns” which allows one to visualize which people belong to the same groups (e.g. building, club, etc..) even if a contact between those individuals has not been reported.