Colorado researchers have published new findings in Emerging Infectious Diseases that take a first look at the use of SARS-CoV-2 mathematical modeling to inform early statewide policies enacted to reduce the spread of the Coronavirus pandemic in Colorado.
Colorado researchers have published new findings in Emerging Infectious Diseases that take a first look at the use of SARS-CoV-2 mathematical modeling to inform early statewide policies enacted to reduce the spread of the Coronavirus pandemic in Colorado. Among other findings, the authors estimate that 97 percent of potential hospitalizations across the state in the early months of the pandemic were avoided as a result of social distancing and other transmission-reducing activities such as mask wearing and social isolation of symptomatic individuals.
The modeling team was led by faculty and researchers in the Colorado School of Public Health and involved experts from the University of Colorado Anschutz Medical Campus, University of Colorado Denver, University of Colorado Boulder, and Colorado State University.
“One of the defining characteristics of the COVID-19 pandemic was the need for rapid response in the face of imperfect and incomplete information,” said the authors. “Mathematical models of infectious disease transmission can be used in real-time to estimate parameters, such as the effective reproductive number (Re) and the efficacy of current and future intervention measures, and to provide time-sensitive data to policymakers.”
The new paper describes the development of such a model, in close collaboration with the Colorado Department of Health and Environment and the Colorado Governor’s office to gage the impact of early policies to decrease social contacts and, later, the impact of gradual relaxation of Stay-at-Home orders. The authors note that preparing for hospital intensive care unit (ICU) loads or capacity limits was a critical decision-making issue.
Read more at University of Colorado Anschutz Medical Campus
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