#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol

  1. Abigail Dommer
  2. Lorenzo Casalino
  3. Fiona Kearns
  4. Mia Rosenfeld
  5. Nicholas Wauer
  6. Surl-Hee Ahn
  7. John Russo
  8. Sofia Oliveira
  9. Clare Morris
  10. Anthony Bogetti
  11. Anda Trifan
  12. Alexander Brace
  13. Terra Sztain
  14. Austin Clyde
  15. Heng Ma
  16. Chakra Chennubhotla
  17. Hyungro Lee
  18. Matteo Turilli
  19. Syma Khalid
  20. Teresa Tamayo-Mendoza
  21. Matthew Welborn
  22. Anders Christensen
  23. Daniel G. A. Smith
  24. Zhuoran Qiao
  25. Sai Krishna Sirumalla
  26. Michael O’Connor
  27. Frederick Manby
  28. Anima Anandkumar
  29. David Hardy
  30. James Phillips
  31. Abraham Stern
  32. Josh Romero
  33. David Clark
  34. Mitchell Dorrell
  35. Tom Maiden
  36. Lei Huang
  37. John McCalpin
  38. Christopher Woods
  39. Alan Gray
  40. Matt Williams
  41. Bryan Barker
  42. Harinda Rajapaksha
  43. Richard Pitts
  44. Tom Gibbs
  45. John Stone
  46. Daniel Zuckerman
  47. Adrian Mulholland
  48. Thomas Miller
  49. Shantenu Jha
  50. Arvind Ramanathan
  51. Lillian Chong
  52. Rommie Amaro
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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 ob-scure 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.

ACM Reference Format

Abigail Dommer1†, Lorenzo Casalino1†, Fiona Kearns1†, Mia Rosenfeld1, Nicholas Wauer1, Surl-Hee Ahn1, John Russo,2Sofia Oliveira3, Clare Morris1, AnthonyBogetti4, AndaTrifan5,6, Alexander Brace5,7, TerraSztain1,8, Austin Clyde5,7, Heng Ma5, Chakra Chennubhotla4, Hyungro Lee9, Matteo Turilli9, Syma Khalid10, Teresa Tamayo-Mendoza11, Matthew Welborn11, Anders Christensen11, Daniel G. A. Smith11, Zhuoran Qiao12, Sai Krishna Sirumalla11, Michael O’Connor11, Frederick Manby11, Anima Anandkumar12,13, David Hardy6, James Phillips6, Abraham Stern13, Josh Romero13, David Clark13, Mitchell Dorrell14, Tom Maiden14, Lei Huang15, John McCalpin15, Christo- pherWoods3, Alan Gray13, MattWilliams3, Bryan Barker16, HarindaRajapaksha16, Richard Pitts16, Tom Gibbs13, John Stone6, Daniel Zuckerman2*, Adrian Mulholland3*, Thomas MillerIII11,12*, ShantenuJha9*, Arvind Ramanathan5*, Lillian Chong4*, Rommie Amaro1*. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. InSupercomputing ‘21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis. ACM, New York, NY, USA, 14 pages.<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://doi.org/finalDOI">https://doi.org/finalDOI</ext-link>

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