Track Editors

Martin Michalowski, University of Minnesota, USA
Robert Moskovitch, Ben-Gurion University, Israel
Nitesh Chawla, University of Notre Dame, USA


The recent outbreak of the novel Coronavirus 2019 (COVID-19) originated in Wuhan, China and within several months spread globally, resulting in millions of confirmed cases and hundreds of thousands of deaths worldwide. The human race is facing one of the most meaningful public health emergencies in the modern era caused by the COVID-19 pandemic. This pandemic introduced various challenges, from lock-downs with significant economic costs to fundamentally altering the way of life for many people around the world. The battle to understand and control the virus is still at its early stages. The uncertainty of why some patients are infected and experience severe symptoms, others are infected but asymptomatic, and others are not infected at all makes managing this pandemic very challenging. Furthermore, the development of treatments and vaccines relies on knowledge generated from an ever evolving and expanding information space.

Given the availability of digital data in the modern era, artificial intelligence (AI) is a meaningful tool for addressing the various challenges introduced by this unexpected pandemic. Some of the challenges include: outbreak prediction, risk modeling (who will get infected and develop symptoms), testing strategy optimization, drug development, treatment repurposing, vaccine development, and others. Since the detected spread of COVID- 19, a meaningful amount of research has been performed that used AI-based methods in the analysis of data, in the attempt to repurpose existing drugs, in understanding who is susceptible to the virus and who will get severe symptoms, and even in developing a cure.

In this special track on Artificial Intelligence and COVID-19, we invited scholars and clinicians to present leading research focusing on the solutions data science and AI provide to address challenges related to the global pandemic, and on relevant deployments and experiences in gearing AI to cope with COVID-19.

Introduction to the Special Track on Artificial Intelligence and COVID-19

Martin Michalowski, Robert Moskovitch and Nitesh V. Chawla

A Metric Space for Point Process Excitations

Myrl G. Marmarelis, Greg Ver Steeg and Aram Galstyan

NLP Methods for Extraction of Symptoms from Unstructured Data for Use in Prognostic COVID-19 Analytic Models

Greg M. Silverman, Himanshu S. Sahoo , Nicholas E. Ingraham, Monica Lupei, Michael A. Puskarich, Michael Usher, James Dries, Raymond L. Finzel, Eric Murray, John Sartori, Gyorgy Simon, Rui Zhang, Genevieve B. Melton, Christopher J. Tignanelli and Serguei VS Pakhomov

Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

Roberto Capobianco, Varun Kompella, James Ault, Guni Sharon, Stacy Jong, Spencer Fox, Lauren Meyers, Peter R. Wurman and Peter Stone

EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

Cédric Colas, Boris Hejblum, Sebastien Rouillon, Rodolphe Thiébaut, Pierre-Yves Oudeyer, Clément Moulin-Frier and Mélanie Prague