Modeling COVID-19 scenarios for the United States
Published October 23, 2020, in Nature Medicine (opens in a new window)
Abstract
We use COVID-19 case and mortality data from 1 February 2020 to 21 September 2020 and a deterministic SEIR (susceptible, exposed, infectious and recovered) compartmental framework to model possible trajectories of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the effects of non-pharmaceutical interventions in the United States at the state level from 22 September 2020 through 28 February 2021. Using this SEIR model, and projections of critical driving covariates (pneumonia seasonality, mobility, testing rates and mask use per capita), we assessed scenarios of social distancing mandates and levels of mask use. Projections of current non-pharmaceutical intervention strategies by state—with social distancing mandates reinstated when a threshold of 8 deaths per million population is exceeded (reference scenario)—suggest that, cumulatively, 511,373 (469,578–578,347) lives could be lost to COVID-19 across the United States by 28 February 2021. We find that achieving universal mask use (95% mask use in public) could be sufficient to ameliorate the worst effects of epidemic resurgences in many states. Universal mask use could save an additional 129,574 (85,284–170,867) lives from September 22, 2020 through the end of February 2021, or an additional 95,814 (60,731–133,077) lives assuming a lesser adoption of mask wearing (85%), when compared to the reference scenario.
Citation
IHME COVID-19 Forecasting Team. Modeling COVID-19 scenarios for the United States. Nature Medicine. 23 October 2020. doi:10.1038/s41591-020-1132-9.
Authors
- Bobby Reiner,
- Ryan Barber,
- Christopher J.L. Murray,
- Stephen Lim,
- Simon Hay,
- James Collins,
- Peng Zheng,
- James Albright,
- Cat Antony,
- Aleksandr Aravkin,
- Steve Bachmeier,
- Marlena Bannick,
- Sabina Bloom,
- Austin Carter,
- Emma Castro,
- Kate Causey,
- Suman Chakrabarti,
- Fiona Charlson,
- Rebecca Cogen,
- Emily Combs,
- Xiaochen Dai,
- William Dangel,
- Lucas Earl,
- Sam Ewald,
- Maha Ezalarab,
- Alize Ferrari,
- Abraham Flaxman,
- Joseph Frostad,
- Nancy Fullman,
- Emmanuela Gakidou,
- John Gallagher,
- Scott Glenn,
- Erik Goosmann,
- Jiawei He,
- Nathaniel Henry,
- Benjamin Hurst,
- Casey Johanns,
- Parkes Kendrick,
- Sam Larson,
- Alice Lazzar-Atwood,
- Kate LeGrand,
- Haley Lescinsky,
- Emily Linebarger,
- Rafael Lozano,
- Rui Ma,
- Johan Månsson,
- Laurie Marczak,
- Molly Miller-Petrie,
- Ali Mokdad,
- Julia Morgan,
- Paulami Naik,
- Chris Odell,
- Aaron Osgood-Zimmerman,
- Sam Ostroff,
- Maja Pašović,
- Louise Penberthy,
- Geoffrey Phipps,
- David Pigott,
- Ian Pollock,
- Rebecca Ramshaw,
- Sofia Redford,
- Sam Rolfe,
- Damian Santomauro,
- Ryan Shackleton,
- David Shaw,
- Brittney Sheena,
- Reed Sorensen,
- Gianna Sparks,
- Emma Spurlock,
- Michelle Subart,
- Ruri Syailendrawati,
- Anna Torre,
- Chris Troeger,
- Theo Vos,
- Alexandrea Watson,
- Stefanie Watson,
- Kirsten Wiens,
- Lauren Woyczynski,
- Liming Xu
Datasets
All our datasets are housed in our data catalog, the Global Health Data Exchange (GHDx). Visit the GHDx to download data from this article.