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.

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.

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