# Scaling COVID-19 rates with population size in the United States

## TL;DR

This study shows how the spread and impact of COVID-19 varied with county population size and age structure in the U.S., offering insights for better public health planning.

## Contribution

The paper introduces a novel analysis of how population size and age structure influence the scaling of infectious disease burdens.

## Key findings

- Larger counties experienced higher case burdens, while smaller counties had higher death burdens.
- Older populations in smaller counties may contribute to increased mortality rates.
- Scaling dynamics of infections and deaths depend on population size and time.

## Abstract

Using county-level data from the United States, we assessed allometric scaling relationships of coronavirus disease (COVID-19) cases, deaths and age structure within and across the first four major waves of the pandemic (wild-type, alpha, delta, omicron). Results generally indicate that the burden of cases disproportionately impacted larger-sized counties, while the burden of deaths disproportionately impacted smaller counties. This may be partially due to multiple interacting social mechanisms, including a higher proportion of older adults who live in smaller counties. Moreover, these likely social mechanisms interacting with vaccinations and virus waves created a dynamic pattern whereby the rate and magnitude of infections and deaths were population- and time-dependent. Our results offer a novel perspective on the scaling dynamics of infectious diseases, highlighting how both the rate and magnitude of COVID-19 cases and deaths scale differently across counties. Population size and age structure are key factors in predicting disease burden. Our findings have practical implications, suggesting that scaling-informed public health policies could more effectively allocate resources and interventions to mitigate the impact of future epidemics across heterogeneous populations.