Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections
Przemyslaw Porebski,
Srinivasan Venkatramanan,
Aniruddha Adiga,
Brian Klahn,
Benjamin Hurt,
Mandy L. Wilson,
Jiangzhuo Chen,
Anil Vullikanti,
Madhav Marathe,
Bryan Lewis
Affiliations
Przemyslaw Porebski
Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA; Corresponding author.
Srinivasan Venkatramanan
Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
Aniruddha Adiga
Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
Brian Klahn
Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
Benjamin Hurt
Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
Mandy L. Wilson
Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
Jiangzhuo Chen
Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
Anil Vullikanti
Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA; Department of Computer Science, University of Virginia, Charlottesville, 22904, VA, USA
Madhav Marathe
Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA; Department of Computer Science, University of Virginia, Charlottesville, 22904, VA, USA
Bryan Lewis
Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, 22911, VA, USA
Scenario-based modeling frameworks have been widely used to support policy-making at state and federal levels in the United States during the COVID-19 response. While custom-built models can be used to support one-off studies, sustained updates to projections under changing pandemic conditions requires a robust, integrated, and adaptive framework. In this paper, we describe one such framework, UVA-adaptive, that was built to support the CDC-aligned Scenario Modeling Hub (SMH) across multiple rounds, as well as weekly/biweekly projections to Virginia Department of Health (VDH) and US Department of Defense during the COVID-19 response. Building upon an existing metapopulation framework, PatchSim, UVA-adaptive uses a calibration mechanism relying on adjustable effective transmissibility as a basis for scenario definition while also incorporating real-time datasets on case incidence, seroprevalence, variant characteristics, and vaccine uptake. Through the pandemic, our framework evolved by incorporating available data sources and was extended to capture complexities of multiple strains and heterogeneous immunity of the population. Here we present the version of the model that was used for the recent projections for SMH and VDH, describe the calibration and projection framework, and demonstrate that the calibrated transmissibility correlates with the evolution of the pathogen as well as associated societal dynamics.
