---
title: "Predictive Analytics for COVID-19 Social Distancing"
authors:
  - "Harold Ze Chie Teng"
  - "Hongchao Jiang"
  - "Xuan Rong Zane Ho"
  - "Wei Yang Bryan Lim"
  - "Jer Shyuan Ng"
  - "Han Yu"
  - "Zehui Xiong"
  - "Dusit Niyato"
  - "Chunyan Miao"
doi: "10.24963/ijcai.2021/716"
url: "https://ijcai.org/proceedings/2021/716"
pdf: "https://ijcai.org/proceedings/2021/0716.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "5016-5019"
track: "Demo Track"
keywords:
  - "Computer Vision: General"
  - "Machine Learning: General"
  - "Human-Computer Interaction: General"
---

# Predictive Analytics for COVID-19 Social Distancing

Harold Ze Chie Teng, Hongchao Jiang, Xuan Rong Zane Ho, Wei Yang Bryan Lim, Jer Shyuan Ng, Han Yu, Zehui Xiong, Dusit Niyato, Chunyan Miao

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 5016-5019. DOI: [10.24963/ijcai.2021/716](https://doi.org/10.24963/ijcai.2021/716)

## Abstract

The COVID-19 pandemic has disrupted the lives of millions across the globe. In Singapore, promoting safe distancing by managing crowds in public areas have been the cornerstone of containing the community spread of the virus. One of the most important solutions to maintain social distancing is to monitor the crowdedness of indoor and outdoor points of interest. Using Nanyang Technological University (NTU) as a testbed, we develop and deploy a platform that provides live and predicted crowd counts for key locations on campus to help users plan their trips in an informed manner, so as to mitigate the risk of community transmission.