Heterogeneity in the Spread of Covid-19: An Agent-Based Approach
When
Where
The bulk of COVID-19 modeling so far has been the large-scale, homogeneous approach. Compartmental models using statistically estimated parameters have had significant success, but there is still great variance between locations in how the epidemic is progressing. Heuristically, this could be due to the difficulties that traditional compartmental models have with small scale heterogeneity. In this presentation, we aim to outline agent-based models that could be used to describe the spread of the disease from the neighborhood up to state-wide scales. We also identify certain benefits and drawbacks, along with connections to other sub-projects.
Helen Zhang will talk about a couple of things:
1) Through the ASA Section on Statistical Learning and Data Science, we are organizing a Webinar series "Data Science In Action In Response to the Outbreak of Covid9". The kick-off date is April 20. Hopefully, I will have a website to share on Tuesday.
2) The special paper call by the Journal of Data Science on the topic of Covid-19 data modeling and analysis.
Please join the seminar on Zoom at: https://arizona.zoom.us/j/183045568
For additional updates, please subscribe to the mailing list for this working group by sending an email to: covid-19-math@list.arizona.edu
Materials available at available on UA Box at https://arizona.box.com/s/cxv4i4aqhk7uty33pc3y8f0rmcb8gs6y
or
https://drive.google.com/drive/folders/1OjQ6ahioBx7INcBvTOEr5UgP5gHg44tb?usp=sharing