Scaling COVID-19 Rates with Population Size in the United States

We assessed Urban Scaling Theory using time-series data by quantifying allometric scaling relationships of coronavirus disease (COVID-19) cases, deaths, and demographic cohorts within and across three major variant waves of the pandemic (first, delta, omicron). Results indicate that with county-level population size in the United States, the burden of cases disproportionately impacted larger-sized counties. In contrast, the burden of deaths disproportionately impacted smaller counties, which may be partially due to a higher proportion of older adults who live in smaller counties. Future infectious disease burden across populations might be attenuated by applying Urban Scaling Theory to epidemiological efforts through identifying disease allometry and concomitant allocation of medical interventions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was partially funded by a University of Arizona University Fellows Award to AC. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All data used are publicly available from the following sources: 1) COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University 2) Kaiser Health News All analyses and datasets are openly available in the following Zenodo repository: I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in Zenodo repository ()