SIAM International Conference on Data Mining Tutorial (SDM 2021)
Studying Spread Patterns of COVID-19 based on Spatiotemporal Data

Location: SDM 2021 (Vitual Conference)

Date: April 29 - May 1, 2021. Tutorial time and room TBA.


The current COVID-19 epidemic have transformed every aspect of our lives, especially our behavior and routines. These changes have been drastically impacting the economy in each region, such as local restaurants and transportation systems. With massive amounts of ambient data being collected everywhere, we now can develop innovative algorithms to have a much greater understanding of epidemic spread patterns of COVID-19 based on spatiotemporal data. The findings will open up the possibility to design adaptive planning or scheduling systems that will help preventing the spread of COVID-19 and other infectious diseases.

In this tutorial, we will review the trending state-of-the-art machine learning techniques to model epidemic spread patterns with spatiotemporal data. These techniques are organized from three parts based on the types from spatio-temporal data:

(1). Data type I (slides ) -- event or process model (inverse reinforcement learning)

(2). Data type II (slides )-- temporal change model (graph neural network method)

(3). Data type III (slides )-- temporal snapshot model (remote sensing data)

Under current epidemic with unknown lasting time, we believe that modeling the spread patterns of COVID-19 epidemic is an important topic that will benefit to researchers and practitioners from both academia and industry.


epidemic spread pattern, spatiotemporal data


Beiyu Lin

Assistant Professor in Computer Science

The University of Texas Rio Grande Valley.

Xiaowei Jia

Assistant Professor in Computer Science

The University of Pittsburgh.

Zhiqian Chen

Assistant Professor in Computer Science

Mississippi State University.

Last Update in Jan 2021