Upcoming Events
Summer Speaker Series - Mason Online Pandemic Modeling Forum
Aug 7, 2020, 3:00 - 4:30 PM
Non-Pharmaceutical Herd Immunity using Homemade Masks: Dale Brearcliffe, Computational Social Science PhD student, George Mason University
The Coronavirus disease 2019 global pandemic in the United States is without a vaccine or cure to prevent its spread. Social distancing and stay at home orders have created financial turmoil while mandatory mask requirements have created other controversies. This paper uses a simple agent-based SEIR model developed using NetLogo to explore the use of homemade masks of various quality in a representative United States population. The goal was to determine if a non-pharmaceutical “herd immunity” can be achieved using homemade masks. Doing so without vaccines can lower even the small risk posed by an eventual vaccination. The model demonstrates that at high levels of adoption, even a mix of questionable quality homemade masks can “flatten the curve” for the pandemic and could do so without the immediate, sever economic cost of staying at home. The model suggests it is possible for a herd immunity effect to cause an early end to the pandemic resulting in fewer affected individuals.
Dale is a data scientist at Logistics Management Institute (LMI) where he conducts data analysis and develops computational models for government clients; creates agent-based models to explore emergent phenomenon in non-linear complex interactions; analyzes law enforcement workforce and crime data. He acquires and analyzes social media information; uses Hadoop tools to extract, merge, to create information from data; conducts social network analysis and applied network science studies. He also creates algorithms in Python, Spark, R, Java, REBOL, and other languages as needed, and visualizes data using Processing and D3.js.
In 2017, Dale received a Masters of Arts in Interdisciplinary Studies with a concentration in computational social science from Mason. He is now pursuing a Ph.D. in the same discipline.
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