Contact patterns during the 2020 COVID-19 epidemic

Guest post by Dr. Eli Fenichel, Yale University

This Success Story is a report on the results of the Northeast Big Data Innovation Hub’s 2020 Seed Fund program.

The goal of the project was to bring together mathematics, data science, economic epidemiology, and public health, contributing to all fields by analyzing smart device contact data to infer behavioral responses to COVID-19 risk and COVID-19 policy. 

Smart device data have attracted a lot of attention during the COVID-19 pandemic. Our project found good uses for these data and also explored their limitations. The NEBD Hub Seed Fund supported the acquisition and analysis of a large smart device dataset, and supported students learning to tackle real-world data problems, data analysis tools, and cloud computing at a large scale. It also supported several studies of pandemic-associated behavior patterns.

The project resulted in an undergraduate thesis (with two more underway), one master’s thesis, and two other students were involved at a lower level of commitment. One publication was produced as a result of this project: Gonsalves, G.S., Copple, J.T., Paltiel, A.D., Fenichel, E.P., Bayham, J., Abraham, M., Kline, D., Malloy, S., Rayo, M.F., Zhang, N. and Faulkner, D., 2021. Maximizing the Efficiency of Active Case Finding for SARS-CoV-2 Using Bandit Algorithms. Medical Decision Making, 41(8), pp.970-977.

A follow-on grant of about $40k was obtained for continuing related work.

Eli Fenichel is the Knobloch Family Professor of Natural Resource Economics at Yale University. His research approaches natural resource management and sustainability as a portfolio management problem by considering natural resources as a form of capital.

Anna Gilbert is the John C. Malone Professor of Mathematics and Statistics & Data Science at Yale University. Her research interests include analysis, probability, discrete mathematics, and algorithms. I am especially interested in randomized algorithms with applications to harmonic analysis, signal and image processing, and massive datasets.

Roy Lederman is an Assistant Professor of Statistics & Data Science at Yale University.  Lederman is interested in the organization and analysis of data, and he is working on computational and modeling problems in cryo-Electron Microscopy, a technology for mapping molecular structures.