With over 8 million cases and 225,000 deaths by the end of October, 2020, the USA has mounted arguably the worst COVID-19 response of any country. Pre-existing sociodemographic disparities in health access and outcomes have been severely exacerbated by the pandemic, and over 40% of COVID-19 mortality has occurred in long term care settings. Governmental response has been largely left to individual states, where it has been susceptible to open politicization of public health recommendations and mandates. As of October 21, a third surge in cases—largely in areas previously spared from the outbreak—threatens once again to overwhelm health care capacity.
As the COVID-19 pandemic took shape in January 2020, CIDDP Co-Directors Hupert and Muckstadt, along with colleague Peter Jackson (Singapore University of Technology and Design), created the first publicly available computational model to assist hospitals in understanding capacity requirements for treating victims of the novel coronavirus. This model, now known as the Cornell COVID Caseload Calculator with Capacity and Ventilators (C5V), was widely used at the State and Federal levels for initial hospital surge calculations. The CIDDP researchers were soon joined by Prof. Michael Klein, who developed the online version of the C5V, extended for a planning horizon up to 360 days with the option to model a second wave. Prof. Klein also worked with a team of students, Prof. Muckstadt and Dr. Hupert to write the first published systematic review of hospital models for COVID.
At the same time, Dr. Hupert helped to establish the Oxford-Cornell-based COVID-19 International Modeling Collaborative (now known as the CoMo Consortium), working with Prof. Lisa J. White. Starting in February 2020, Drs. Hupert and White customized the first version of the CoMo Model for use in the United States, specifically for New York City and State. Since then, Dr. Hupert, now its Lead for Translational Science and Policy, has supported the development of the CoMo application and Consortium, in particular developing the hospital-based components of the model. CoMo-based analyses for New York City, State, and the USA as a whole have continued to inform selected policy discussions at the State and National levels; in addition, Dr. Hupert has led or assisted in modeling of the pandemic in Haiti, Mexico, Malaysia, Syria, and Ecuador.
Early in the pandemic, Dr. Hupert, working with mathematical epidemiologists Alex Washburne and Justin Silverman, used public syndromic surveillance data from NYC and the U.S. Centers for Disease Control and Prevention (CDC) to mathematically predict the early spread of COVID-19 across the United States. The resulting paper argued that SARS-CoV-2 was much more widely spread in the US than current testing had revealed, garnering significant media and scientific attention (6th highest Altimetric score for all papers published in Science Translational Medicine).