Pandemic Informatics: Variants of Concern (VOC)
This post was originally published in the CCC Blog.
Contributions to this post were provided by Elizabeth Bradley (University of Colorado Boulder), Madhav Marathe (University of Virginia), Melanie Moses (The University of New Mexico), William D. Gropp (University of Illinois Urbana-Champaign), and Daniel Lopresti (Lehigh University).
We are pleased to announce the second addendum to the Computing Research Association (CRA) and Computing Community Consortium (CCC) Pandemic Informatics: Preparation, Robustness, and Resilience quadrennial paper on variants of concern (VOC).
A year ago, few experts correctly predicted the toll the pandemic has now taken, nor the extraordinarily rapid development and administration of effective vaccines. Scientists have dramatically increased understanding of the SARS-CoV-2 virus, treatment, and vaccines. Yet, where the pandemic will be a year from now remains very difficult to predict, due in large part to rapidly spreading variants of concern (VOC).
The B.1.1.7, B.1.351, P.1, B.1.427, and B.1.429 variants currently circulating in the United States are classified as variants of concern by the CDC; they have some combination of increased transmissibility, higher mortality, and/or ability to overcome immunity from prior infection or vaccination to varying degrees. Models developed by computer scientists, working in close collaboration with epidemiologists, virologists, data scientists, and immunologists, are essential in guiding us to more-effective strategies for tracking and future vaccination campaigns and helping us understand where VOC emerge, how fast they spread, and how much they evade protection from vaccination or prior infection.
See the Pandemic Informatics: Variants of Concern (VOC) addendum here for more details.