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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 FormatAbigail Dommer1{dagger}, Lorenzo Casalino1{dagger}, Fiona Kearns1{dagger}, Mia Rosenfeld1, Nicholas Wauer1, Surl-Hee Ahn1, John Russo,2 Sofia 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 OConnor11, 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. In Supercomputing 21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis. ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI
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BackgroundDuring the coronavirus disease 2019 (COVID) pandemic, various organisations have produced management guidance for cancer patients and the delivery of cytotoxic chemotherapy, but none offer estimates of risk, or the potential impact across populations. MethodsWe combine data from four countries to produce pooled age-banded Case Fatality Rates (CFRs), calculate the sex-difference in survival and use data from four recent studies to convert CFRs into age-sex stratified Infection Fatality Rates (IFRs). We estimate the additional risk of death in cancer patients, and in those receiving chemotherapy. We illustrate the impact of these by considering the impact on a national incident cancer cohort and present some clinical scenarios. ResultsWe obtained data based on 412,985 cases and 41,854 deaths. The pooled estimate for IFR was 0.92%. Age-related IFRs for patients with cancer range from 0.01% to 29%, and higher in patients receiving chemotherapy. The risk is significantly higher in men than women. 40% of all male and 32% of all female patients with a new diagnosis of cancer this year have an IFR of [≥] 5%. ConclusionsOlder male patients are at a higher risk of death with COVID infection. Patients with cancer are also at higher risk, as are those who have recently received chemotherapy. We provide well-founded estimates to allow patients and clinicians to better balance these risks, and illustrate the wider impact in a national incident cohort. FUNDING & DISCLOSURESMW receives funding from the Imperial/ NIHR BRC; SD receives funding from the IC/ICR CRUK Major Centre; LPS receives funding from Brain Tumour Research and the Brain Tumour Research Campaign. JC is supported by the Guangdong International Young Research Talents Training Programme for Postdoctoral Researchers. The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Code, data and appendicies are available at: https://gitlab.com/computational.oncology/covidcancerrisk HIGHLIGHTSO_LIWe report case and infection fatality rates based on a large multi-national cohort C_LIO_LIWe provide sex and age-specific estimates of risk C_LIO_LIWe provide estimates of additional risk for patients with cancer to allow patients and clinicians to balance risk and benefit C_LI