The COVID-19 pandemic has emerged as one of the greatest public health challenges of modern times. Policy makers rely on measuring how quickly the disease is spreading to make decisions about mitigation strategies . We analyze US county-level data about confirmed COVID-19 infections and deaths  to show that its impact is heterogeneous. A small fraction of counties represent the majority of all infectionsand deaths. These hot spotsare correlated with populous areas where the disease arrives earlier and grows faster. When county-level data is aggregated to create state-level and national statistics, these hot spots systematically bias the growth rates. As a result, infections and deaths appear to grow faster at those larger scales than they do within typical counties that make up those larger regions . Public policy, economic analysis and epidemic modeling have to account for potential distortions introduced by spatial aggregation.
The COVID-19 pandemic has emerged as a global public health crisis. To make decisions about mitigation strategies and to understand the disease dynamics, policy makers and epidemiologists must know how the disease is spreading in their communities . We analyze confirmed infections and deaths over multiple geographic scales to show that COVID-19's impact is highly unequal: many subregions have nearly zero infections, and others are hot spots. We attribute the effect to a Reed-Hughes-like mechanism in which disease arrives at different times and grows exponentially. Hot spots, however, appear to grow faster than neighboring subregions and dominate spatially aggregated statistics, thereby amplifying growth rates. The staggered spread of COVID-19 can also make aggregated growth rates appear higher even when subregions grow at the same rate . Public policy, economic analysis and epidemic modeling need to account for potential distortions introduced by spatial aggregation.
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one of the greatest public health challenges of modern times. Policy makers rely on measuring how quickly
a global public health crisis. To make decisions about mitigation strategies and to understand the disease dynamics, policy makers and epidemiologists must know how
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to make decisions about mitigation strategies
in their communities
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US county-level data about confirmed COVID-19
confirmed
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over multiple geographic scales
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its impact is heterogeneous. A small fraction of counties represent the majority of all infectionsand deaths. These hot spotsare correlated with populous areas where the disease arrives earlier and grows faster. When county-level data is aggregated to create state-level and national statistics, these hot spots systematically bias the
COVID-19's impact is highly unequal: many subregions have nearly zero infections, and others are hot spots. We attribute the effect to a Reed-Hughes-like mechanism in which disease arrives at different times and grows exponentially. Hot spots, however, appear to grow faster than neighboring subregions and dominate spatially aggregated statistics, thereby amplifying
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As a result, infections and deaths appear to grow faster at those larger scales than they do within typical counties that make up those larger regions
The staggered spread of COVID-19 can also make aggregated growth rates appear higher even when subregions grow at the same rate
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have
need