How can we design an accessible way to evaluate the effectiveness of face masks and face shields against infectious particles?
Data Visualization & Analysis | Research | Communication | Leadership
In response to the urgent demand for reliable PPE in March 2020 caused by the rapid spread COVID-19, I led the co-founding of the Coalition for Health Innovation in Medical Emergencies (CHIME). From March to December 2020, I facilitated cross-department and cross-organizational collaboration within CHIME between the Yale CEID, the Yale-New Haven Hospitals, Unilever, and the United Nations Development Programme (UNDP) in Colombia. My largest contributions were on the effort to develop an accessible method for testing the efficiency of N95 alternatives and the development of a systematic approach to CAD-based air particle simulation around plastic face shields. The results and procedural recommendations were published in the Nature Journal of Exposure Science and Environmental Epidemiology, for which I am listed as a co-author.
A shortage of masks and PPE to fight COVID-19 led to a surplus of donations – but no way to tell which were counterfeit, effective, or breathable.
I worked closely with three research scientists in mechanical, chemical, and environmental engineering to develop and manufacture a reproducible method for evaluating the filtration efficiency and breathability of a given mask or permeable cloth material. This system was designed to closely replicate the standard NIOSH N95 certification procedure but with more accessible lab equipment so that other university or hospital institutions could replicate the test.
As of December 2020, over 120 unique surgical and cloth masks have been tested using the CHIME-published system. I assisted with testing and mask sample preparation, and processed the quantitative results that are reported directly to the Yale-New Haven Hospital and Yale Environmental Health & Safety.
I created a majority of the figures and graphs presented in the published procedures of this project, and conducted most of the data analysis and visual plotting in python and matlab.
To increase testing efficiency to meet high demand for evaluated masks, I used data analysis in python to present quantitative evidence that the results from our original testing procedure closely correlated with the results of the ‘rapid’ mask test developed by CHIME in May, 2020.
A software approach: simulating air particles around potential face shield designs
Concurrently, I led a team of two undergraduates to assist with the development and application of a systematic method for modeling and evaluating the protectiveness of plastic face shields against infectious particles. In this capacity, I served as both a mentor to the undergaduate engineering students during their 2020 CEID Summer Fellowship and the project lead responsible for teaching them the SolidWorks CAD and Computational Fluid Dynamics (CFD) modeling. I also acted as the Yale liason to the Colombia UNDP and ultimately authored the report of our findings and procedures to the UNDP in English and Spanish.
All face shields were measured and modeled in CAD. Our evaluation procedure involved visual and quantitative analysis of jet stream behavior of air particles when fired at different angles towards and from the eyes, nose, and mouth of the CAD mannequin. Shields with a tendency to redirect air particles into the face of the subject were reported to be insufficient as PPE.