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Data for the People: Modeling, Math, and Social Mixing
This week on D4P, 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. More specifically, they will present a case study from The Lancet published on March 25, 2020 that applied a model to predict what would happen in Wuhan, China under different scenarios of social mixing (i.e. when and how the population stops social distancing). You can see this paper in advance through this link: DOI:https://doi.org/10.1016/S2468-2667(20)30073-6

Data for the People (D4P) is a weekly, interactive web-series that invites ‘the people’ (all of our community members, worldwide!) to engage with relevant scientific research topics. Scientific research topics are presented by scientists in our communities— directly from the primary scientific literature — and explored in a way that maintains the rigor of the research, while eliminating the inaccessible jargon.

Explore past D4P webinars on our website: https://rockedu.rockefeller.edu/d4p/.

May 20, 2020 03:30 PM in Eastern Time (US and Canada)

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