From the RockEDU event page:
“This week on Data for the People, postdocs Chloe Pasin and Sinead Morris will talk about how scientists are applying hypothetical modeling and math to predict what can happen when social distancing rules are lifted at different time points, and using different strategies. More specifically, they will present a case study from The Lancet published on March 25, 2020 that applied a specific model to predict what would happen in Wuhan, China under different scenarios of social mixing (i.
Event: Summer bootcamp for infectious disease modeling
Location: The Institute of Statistical Mathematics, Tokyo
Event URL: https://sites.google.com/site/modelinfection/home/shortcourse4
Lecture: The lecture introduced students to ways in which the SIR model can be applied to real disease outbreaks. I focussed on a past project that developed a statistical SIR framework to model the spatiotemporal dynamics of dolphin morbillivirus. The slides from the talk are below.