Upcoming Events
Pandemic-Related Data and Computing
Aug 27, 2021, 3:00 - 4:30 PM
Hi everyone and welcome to the start of the fall colloquium series. Though the regular Friday seminars will return as George Mason University campuses open to in-person instruction, please keep in mind the following. This seminar is offered for credit to CSS and CSI graduate students, who are invited to attend in-person or via Zoom. Before the pandemic, the majority of participants in this seminar were not attending for credit, people simply interested in the material presented. For the coming semester, people who are not registered for the seminar but who wish to participate in the presentations are encouraged not come to campus to attend the talks in person since the face-to-face sections are held in a room that is set up to be COVID compliant for 10 attendees. Questions about Mason COVID policies can be answered here: https://www2.gmu.edu/safe-return-campus/personal-and-public-health/face-coverings
Our lead off speaker for this semester will be Professor Robert Axtell, Department of Computational and Data Sciences and External Professor at the Santa Fe Institute. He will be presenting "Pandemic-Related Data and Computing: Lessons Learned from 18 Months of Modeling the SARS-CoV2 virus and COVID-19 Disease." His talk is scheduled for this Friday, August 28 from 3-4:30 p.m. (see below for virtual details).
We hope you can join us on Friday, August 27 at 3:00 p.m.
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Date | Friday, August 27, 2021 |
Time | 3:00 pm - 4:30 pm EDT (UTC-4:00) |
Title | Pandemic-Related Data and Computing: Lessons Learned from 18 months of Modeling the SARS-CoV-2 virus and COVID-19 disease |
Speaker | Professor Rob Axtell, Department of Computational and Data Sciences at George Mason and External Professor at the Santa Fe Institute |
Abstract | In March of 2020 an agent-based model from Imperial College claimed that millions of deaths due to COVID-19 disease could be avoided by various social distancing and related policies aimed at limiting interactions between people in order to attenuate the spread of the SARS-CoV-2 virus. Subsequently, many ’natural experiments’ were conducted with country-specific policies with hopes of limiting the health impacts of the virus while not crippling economies. As pandemic-related data on testing, infection, hospitalization, and mortality rates became available, models were built to explain how the pandemic was unfolding. These data were of disparate quality and provided rather weak foundations for model-building. The resulting models tended to focus on health effects, sometimes with economic costs, while economic effects models typically featured rather stylized representations of the underlying epidemic dynamics. Policies evolved to deal with local events but were often little coordinated between countries, while policy disagreements across local, state, and national governments contributed to heterogeneous compliance rates with measures designed to limit infection rates. Such behavioral heterogeneity emerged as a first-order effect as multiple waves of outbreak savaged many regions or whole countries. Even as medical advances arrived that effectively limited both the spread of the virus (vaccines) and progress of the infection in people (e.g., anti-virals), behavioral resistance was fomented in various ways, often through misinformation propagated via social media. In this talk I will review lessons learned from more than 50 distinct attempts to model the pandemic as presented in the Mason Pandemic Modeling Forum over the past year and one-half. The current pandemic represents a canonical case of a coupled human-natural system and has exposed societal vulnerabilities that were previously not well understood. |