RAPID: Open Research Infrastructure for COVID-19 Ventilator Data

Mathematical and Physical Sciences - The lungs are a key avenue of attack for the SARS-CoV-2 virus. Respiratory problems are primary symptoms of COVID-19, and early indication is that it does not behave like previous examples of Acute Respiratory Distress Syndrome (ARDS). A severe urgency exists to understand how to provide optimal care for patients requiring artificial ventilation, to minimize both mortality and adverse long-term effects on those patients who survive. The project will illuminate lung function under the stress of COVID-19 and provide open tools to engage the larger community to help understand this very urgent societal problem. The project output will include instrumentation advances, software and data, as well as models of lung function under the stress of COVID-19. The project will also inform the medical community as to how to treat COVID-19 patients, because COVID-19 differs notably from prior experience with ARDS.

Respiration and lung function is fundamentally a dynamical physical system amenable to traditional pressure/volume/flow relationships, with a quantity called "lung compliance." COVID-19 is unique, in that the underlying biology can lead to changes in the parameters of this dynamical system that are surprisingly fast, and different from previous ARDS cases, on the time scale of hours or days. Medical personnel need to navigate the evolving nature of the consequences of the viral infection as well as mechanical ventilation induced lung inflammation and potential injury, with outcomes ranging from recovery with varying impacts on post-illness lung function to death. This project consists of three related activities: (1) Instrumentation: Continued development of a low-cost, open-source ventilator monitor, including additional options for readily sourceable parts, and related documentation on calibrations. (2) Data: Development, with the broader community, of open datasets of breathing and ventilator data, including flow, pressure, O2 levels, and derived quantities of interest to enable innovation and machine learning in a space that otherwise lacks open data. (3) Models: Development of open simulations, visualizations, and models for mechanical ventilation and the breathing process, under stresses like COVID-19, that enable a physicist's understanding of the system, enable innovation, and can potentially aid the medical community.

This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplement allocated to MPS.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.