How might subway restrictions reduce hospitalization rates? Existing toy SIR models (https://youtu.be/gxAaO2rsdIs)provide great intuitions about how infections spread by simulating different cause-and-effect scenarios under simplifying assumptions. A team led by Prof. David Yao and Engineering student Elioth Sanabria aims to develop a simulation model more germane to NYC by using real data.
Modeling Objectives:
1. Model movement of populations in NYC. To do this, the team is mining a variety of data sets: subway data (entries/exits at each station), demographic data (by neighborhood), hotspot locations (e.g. hospitals, and Google Cloud API’s opening hours and ‘popular times’ at supermarkets), individual mobility data (City-wide mobility survey), and potentially de-identified patient data.
2. Predict outcomes of COVID-19 in NYC (e.g. hospitalization rates) as a function of interventions (e.g. measures that reduce population density in parts of the city).
Deliverables: Interactive web application with visuals to help the public understand infection dynamics and possible cause-and-effect scenarios.
