By analyzing demographic patterns and neighborhood information in NYC, and comparing it with the COVID-19 infections, we can help other cities mitigate outbreaks the future.
Project in MIT COVID-19 Challenge
Aarthe Jayaprakash (aarthej@gmail.com)
Fadoua Khmaissia (khmaissiafadoua@gmail.com)
Pegah Sagheb (pegah86@gmail.com)
Selina Wu (zhenweiwu33@gmail.com)
- Census Bureau (2018) [link]
- Citybike Data (2020) link
- Subway Data (2013-2018)
- Covid19 by zipcode link
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If you find this repository useful in your research, please cite:
@article{khmaissia2020unsupervised,
title={An Unsupervised Machine Learning Approach to Assess the ZIP Code Level Impact of COVID-19 in NYC},
author={Khmaissia, Fadoua and Haghighi, Pegah Sagheb and Jayaprakash, Aarthe and Wu, Zhenwei and Papadopoulos, Sokratis and Lai, Yuan and Nguyen, Freddy T},
journal={arXiv preprint arXiv:2006.08361},
year={2020}
}