The effect of cities and distance on COVID-19 spreading in the United States

TL;DR

This study analyzes how COVID-19 spread across US counties, revealing that urban areas significantly influence rural ones over time, with population size being more impactful than distance.

Contribution

It introduces a cross-correlation analysis method to detect inter-county influences and identifies temporal phases with distinct correlation patterns in COVID-19 spread.

Findings

Strong correlations emerged mainly between urban areas initially.

Over time, influence from urban to rural areas increased.

Population size had a greater effect than geographic distance.

Abstract

The COVID-19 pandemic has evolved over time through multiple spatial and temporal dynamics. The varying extent of interactions among different geographical areas can result to a complex pattern of spreading so that influences between these areas can be hard to discern. Here, we use cross-correlation analysis to detect synchronous evolution and potential inter-influences in the time evolution of new COVID-19 cases at the county level in the USA. Our analysis identified two main time periods with distinguishable features in the behavior of correlations. In the first phase, there were few strong correlations which only emerged between urban areas. In the second phase of the epidemic, strong correlations became widespread and there was a clear directionality of influence from urban to rural areas. In general, the effect of distance between two counties was much weaker than that of the counties population. Such analysis can provide possible clues on the evolution of the disease and may identify parts of the country where intervention may be more efficient in limiting the disease spread.