#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol
Abigail C. Dommer, Lorenzo Casalino, Fiona L. Kearns, Mia A. Rosenfeld, Nicholas Wauer, Surl-Hee Ahn, John D. Russo, A. Sofia F. Oliveira, Clare K. Morris, Anthony T. Bogetti, Anda Trifan, Alexander Brace, Terra Sztain, Austin Clyde, Heng Ma, Chakra Chennubhotla, Hyungro Lee, Matteo Turilli, Syma Khalid, Teresa Tamayo-Mendoza, Matthew Welborn, Anders S. Christensen, Daniel G. A. Smith, Zhuoran Qiao, Sai Krishna Sirumalla, Michael B. O’Connor, Frederick R. Manby, Anima Anandkumar, David J. Hardy, J. C. Phillips, Abraham C. Stern, Josh Romero, David Clark, Mitchell Dorrell, Tom Maiden, Lei Huang, John D. McCalpin, Christopher Woods, Alan Gray, Matt Williams, Bryan Barker, Harinda Rajapaksha, Richard Pitts, Tom Gibbs, John E. Stone, Daniel M. Zuckerman, Adrian J. Mulholland, Thomas F. Miller, Shantenu Jha, Arvind Ramanathan, Lillian T. Chong, Rommie E. Amaro
Abstract
We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.