Wednesday, May 20, 2020 @ 3:30pm ET
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.e. when and how the population stops social distancing).
About our D4P Fellows:
Chloé Pasin, PhD (she/her), Postdoc @ Columbia University University
Chloé is a postdoctoral research scientist at Columbia University. She has a background in mathematics and is now trying to answer concrete clinical questions in infectious disease by using biostatistics and mathematical models. She loves the collaborative aspects of her work, as it brings together people with different background and expertise (immunologists, infectious disease specialists, mathematicians…). She grew up in Toulouse, South West of France and lived in different places (Paris, Bordeaux, Australia, Seattle WA) before moving to NYC. She is currently staying home with her partner and his cat, cooking a lot and practicing more yoga!
Sinead Morris, PhD (she/her), Postdoc @ Columbia University University
Sinead is a postdoctoral research scientist at Columbia University. She uses mathematical models to understand how our immune systems respond to infectious diseases such as HIV and the flu. She grew up in Glasgow, Scotland, before moving to the US in 2013 to start her PhD. It was here that she first developed an interest in using maths to understand how viruses spread through populations, and how best to control them. She is currently sheltering at home with her boyfriend and their dog, Dixie, and in her spare time is rediscovering her love of drawing.