COPSS-NISS COVID-19 Data Science Webinar Series

[Please Note: This session has already occurred.  Go to the News Story for this event to read about and to access a recording and speaker slides from the session.]

Transmission Dynamics of SARS-CoV-2: Inference and Projection

About this Webinar Series

The COPSS-NISS COVID-19 Data Science webinar series is co-organized by the Committee of the Presidents of Statistical Societies (COPSS) and its five charter member societies (ASA, ENAR, IMS, SSC, and WNAR), as well as NISS.  This bi-weekly seminar features the latest research that is positioned on the cusp of new understanding and analysis of COVID-19 pandemic data, and promotes data-driven research and decision making to combat COVID-19. Find out more about this series and view all the previous sessions on the Webinar Series page.

Overview

Dynamic models of infectious disease systems are often used to study the epidemiological characteristics of disease outbreaks, the ecological mechanisms and environmental conditions affecting transmission, and the suitability of various mitigation and intervention strategies.  In recent years these same models have been employed to generate probabilistic forecasts of infectious disease incidence at the population scale.  Here I present research from my own group describing application of model systems and combined model-inference frameworks to the study of SARS-CoV-2.

Participants

Speaker

Jeffrey Shaman, Professor
Environmental Health Sciences (in the International Research Institute for Climate and Society/Earth Institute)
Director, Climate and Health Program
Columbia University

Bio: Jeffrey Shaman is a Professor in the Department of Environmental Health Sciences and Director of the Climate and Health Program at the Columbia University Mailman School of Public Health. He studies the survival, transmission and ecology of infectious agents, including the effects of meteorological and hydrological conditions on these processes. Work-to-date has primarily focused on mosquito-borne and respiratory pathogens. He uses mathematical and statistical models to describe, understand, and forecast the transmission dynamics of these disease systems, and to investigate the broader effects of climate and weather on human health.

Discussant

Roni Rosenfeld, Professor and Head
Machine Learning Department, School of Computer Science
Carnegie Mellon University

Moderator

Lily Wang, Professor
Department of Statistics
Iowa State University

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Organizing Committee

Xihong Lin (Chair) (IMS), Harvard University 
Karen Bandeen-Roche (NISS), Johns Hopkins University 
Chris Barker (ASA), Statistical Planning and Analysis Services, Inc
Gary Chan (WNAR), University of Washington 
Rob Deardon (SSC), University of Calgary 
Natalie Dean (COPSS), University of Florida
Debashree Ray (COPSS), Johns Hopkins University
Jie Peng (WNAR), University of California at Davis 
Nathaniel Stevens (SSC), University of Waterloo 
Elizabeth Stuart (ENAR), Johns Hopkins University 
Ryan Tibshirani (IMS), Carnegie Mellon University  
Lily Wang (ASA), Iowa State University 
Lingzhou Xue (NISS), Pennsylvania State University
Lili Zhao (ENAR), University of Michigan 
Glenn Johnson (Web Communications), NISS