COVID-19 resources

Data Science TAE COVID-19 Response/Resources

We at the Data Science Transdisciplinary Area of Excellence are committed to leveraging data science and computing to assist our community partners at all level in their response to the COVID-19 pandemic. At the same time, we are responsible for informing our faculty and researchers on how to mobilize data science and computing to tackle the novel coronavirus and support a robust recovery of our economy and society from it.

For our partners: We are posting on this webpage a sample collection of researchers and their featured projects, to showcase what Binghamton University can do for the community in this fight. Many of these activities are at their initial stages and have not gone through peer reviews. These works are evolving very quickly.

For our faculty and researchers: We have complied a list of COVID-19-related resources, funding opportunities, networks and virtual events at the bottom of the page. We hope that you will find it useful.

Featured researchers and projects

Hiroki Sayama

Professor of Systems Science and Industrial Engineering
Expertise: mathematical modeling and analysis of dynamical systems (including epidemic dynamics, social networks, etc.), time series analysis, agent-based and network simulation, complex systems


  • Supervising a team of data analysts at UHS for prediction of number of confirmed cases of COVID-19 in Broome County
  • SUNY COVID-19 seed funding project (funded in collaboration with Professor Shelley Dionne and Distinguished Professor Fran Yammarino, both in the School of Management)
    • How to Resume and Maintain Economic Activities in the COVID-19 Era: An Adaptive Social Distancing Approach

Chengbin Deng

Associate Professor of Geography
Expertise: geovisualization, spatial data science, and remote sensing.


  • Map the spatial distribution of COVID-19 and social vulnerability for local communities in the Greater Binghamton Area.
  • Help people in local community in Broome County by providing timely information about free food and other useful resources to get through this unprecedented difficult time. Website

Mapping spatial distribution of COVID-19

Cartogram of COVID-19

Jeremy Blackburn

Assistant Professor of Computer Science
Expertise: large-scale measurements, social media, data science

Project: Understanding the evolution of sinophobic hate speech in social media. This project aims to understand how hate speech, specifically sinophobic language, has evolved online in response to the COVID-19 crisis. Our early results indicate that there has been a meaningful increase in sinophobic language, including new words and phrases, on
fringe Web communities. Some of this is also seen on mainstream web communities like Twitter.


Steam graph 

China graph

Anand Seetharam and Arti Ramesh

Assistant Professors of Computer Science

Expertise: Machine Learning, Data Science


  • Ensemble Regression Models for Short-term Prediction of Confirmed COVID-19 Cases: accurately predicting the number of new COVID-19 cases is critical to understanding and controlling the spread of the disease as well as effectively managing scarce resources (e.g., hospital beds, ventilators). To this end, we design a regression based ensemble learning model consisting of Linear regression, Ridge, Lasso, ARIMA, and SVR that takes the previous 14 days' data into account to predict the number of new COVID-19 cases in the short-term. The ensemble model outputs the best performance by taking into account the performance of all the models. We consider data from top 50 countries around the world that have the highest number of confirmed cases between January 21, 2020 and April 30, 2020. Our results in terms of relative percentage error show that the ensemble method provides superior prediction performance for a vast majority of these countries with less than 10% error for 6 countries and less than 40% error for 27 countries. 
  • Investigating Societal Impact of COVID-19: we collect and study Twitter communications to understand the socio-economic impact of COVID-19 in the United States during the early days of the pandemic. Our analysis reveals that COVID-19 gripped the nation during this time as is evidenced by the significant number of trending hashtags. With infections soaring rapidly, users took to Twitter asking people to self isolate and quarantine themselves. Users also demanded closure of schools, bars, and restaurants as well as lockdown of cities and states. The communications reveal the ensuing panic buying and the unavailability of some essential goods, in particular toilet paper. We also observe users express their frustration in their communications as the virus spread continued. We methodically collect a total of 530,206 tweets by identifying and tracking trending COVID-related hashtags. We then group the hashtags into six main categories, namely 1) General COVID, 2) Quarantine, 3) Panic Buying, 4) School Closures, 5) Lockdowns, and 6) Frustration and Hope, and study the temporal evolution of tweets in these hashtags. We conduct a linguistic analysis of words common to all the hashtag groups and specific to each hashtag group. Our preliminary study presents a succinct and aggregated picture of people’s response to the pandemic and lays the groundwork for future fine-grained linguistic and behavioral analysis.

Website link

Plamen Nikolov

Assistant Professor of Economics

Expertise: economic epidemiology, economics of infectious diseases, behavioral economics, psychology and economics, and field experiments. My research lab conducts economic research at the intersection of health, labor, development and psychology with a focus on improving human welfare by understanding human behavior better and ensuring that policy is informed by better scientific evidence.    

Project: Working on an ongoing experimental intervention examining the role of behavioral biases (how people understand probabilities, gains and losses, present versus future gains), risk preferences, social preferences and time preferences on (the demand for) social distancing. 

Lucius Willis

Computer Cartographer in Geography

Expertise: cartography

Project: Produce planning maps at the request of the Food Bank of the Southern Tier to help them try to anticipate changes in food insecurity as a result of Covid-19.

Ivan Korolev

Assistant Professor of Economics

Expertise: econometrics, applied microeconomics

Project: Use SEIR type models to simulate and predict the spread of infectious disease

Jian Li

Assistant Professor of Electrical and Computer Engineering

Expertise: network science; data analytics with machine learning

Project: Understand and predict the spread of COVID-19 pandemic using network intelligence from invisible ties

Xingye Qiao

Associate Professor of Mathematical Sciences

Expertise: statistics and machine learning

Project: Design a multi-stage sampling plan to estimate the prevalence of COVID-19 in Broome County by making using of both administered data (tests prescribed by physicians only to patients with symptoms) and a pure random sample

Yong Wang

Assistant Professor of Systems Science and Industrial Engineering

Expertise: data analytics

David Schaffer

Visiting Research Professor with the Institute for Justice and Well-Being

Expertise: evolutionary computation, for pattern discovery and many other applications. Speech processing and computational linguistics for finding patterns in speech/text. Spiking neural networks.

Kenneth Kurtz

Professor of Psychology

Expertise: My lab has skills in applying non-conventional neural networks to machine learning/prediction problems.

Changqing Cheng

Assistant Professor of Systems Science and Industrial Engineering

Expertise: large-scale simulation, nonlinear dynamics analysis of the pandemic evolution, and the optimal control

Saeideh Mirghorbani

Assistant Professor, School of Management

Expertise: healthcare systems using stochastic decision-making processes. Supply chain management and operations management systems.

Jenny Jiao

Assistant Professor, School of Management

Expertise: emotion, decision making, social connection, loneliness and decision bias

Resources, funding opportunities, networks and virtual events