SARS-CoV-2 modeling: What have we learned from this pandemic about how (not) to model disease spread?

The SARS-CoV-2 pandemic is awash in data, including daily, spatially-resolved COVID case data, virus sequence data, patients `omics data, and mobility data. Journals are now also awash in studies that make use of quantitative modeling approaches to gain insight into the geographic spread of SARS-CoV-2 and its temporal dynamics, as well as studies that predict the impact of control strategies on SARS-CoV-2 circulation. Some, but by no means all, of these studies are informed by the massive amounts of available data. Some, but by no means all, of these studies have been useful — in that their predictions revealed something beyond simple back of the envelope calculations. To summarize some of these findings, in this symposium, we will address questions such as: What do we want from models of disease spread? What can and should be predicted? Which data are the most useful for predictions? When do we need mechanistic models? What have we learned about how to model disease spread from unmet and/or conflicting predictions? The symposium speakers will explore these questions from different perspectives on what data need to be considered and how models can be evaluated. Each speaker will deliver a 10-minute talk with ample time set aside for moderated questions/discussion.


When: Thursday, January 21, from 10am to 2pm ET

Where: We will live stream through the Emory TMLS YouTube Channel . Please register at the link below to be updated of any changes. 


More Info: will be posted at


Organizers: Katia Koelle, Daniel Weissman, Rustom Antia (Emory University)


            Rachel Baker (Princeton University)

            Caroline Buckee (Harvard University)

            Sarah Cobey (University of Chicago)

            Nigel Goldenfeld and Sergei Maslov (UIUC)

            Ruian Ke (LANL)

            Stephen Kissler (Harvard University)

            Lauren Ancel Meyers (University of Texas)

            Isabel Rodriguez-Barraquer (UCSF)

            Sam Scarpino (Northeastern University)

            Michael Worobey (University of Arizona)

            Joshua Weitz (Georgia Institute of Technology)

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