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Jeffrey C Cegan,1 Benjamin D Trump,1 Susan M Cibulsky,2 Zachary A Collier,3 Christopher L Cummings,4 Scott L Greer,5 Holly Jarman,5 Kasia Klasa,1,5 Gary Kleinman,2 Melissa A Surette,6 Emily Wells,1 Igor Linkov1 1US Army Engineer Research and Development Center, US Army Corps of Engineers, Vicksburg, MS, USA; 2US Department of Health and Human Services, Office of the Assistant Secretary for Preparedness and Response, Boston, MA, USA; 3Radford University, Davis College of Business and Economics, Department of Management, Radford, VA, USA; 4North Carolina State University, Genetic Engineering and Society Center, Raleigh, NC, USA; 5University of Michigan, School of Public Health, Department of Health Management and Policy, Ann Arbor, MI, USA; 6Federal Emergency Management Agency, Region I, Boston, MA, USACorrespondence: Jeffrey C Cegan; Igor LinkovUS Army Engineer Research and Development Center, US Army Corps of Engineers, 696 Virginia Road, Concord, MA, 01742, USATel +1-978-318-8881; +1-617-233-9869Email Jeffrey.C.Cegan@usace.army.mil; Igor.Linkov@usace.army.milAbstract: Many efforts to predict the impact of COVID-19 on hospitalization, intensive care unit (ICU) utilization, and mortality rely on age and comorbidities. These predictions are foundational to learning, policymaking, and planning for the pandemic, and therefore understanding the relationship between age, comorbidities, and health outcomes is critical to assessing and managing public health risks. From a US government database of 1.4 million patient records collected in May 2020, we extracted the relationships between age and number of comorbidities at the individual level to predict the likelihood of hospitalization, admission to intensive care, and death. We then applied the relationships to each US state and a selection of different countries in order to see whether they predicted observed outcome rates. We found that age and comorbidity data within these geographical regions do not explain much of the