Abstract
The COVID-19 pandemic has posed unprecedented challenges to public health world-wide. 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. Here we analyse confirmed infections and deaths over multiple geographic scales to show that COVID-19’s impact is highly unequal: many regions have nearly zero infections, while others are hot spots. We attribute the effect to a Reed–Hughes-like mechanism in which the disease arrives to regions at different times and grows exponentially at different rates. Faster growing regions correspond to hot spots that dominate spatially aggregated statistics, thereby skewing growth rates at larger spatial scales. Finally, we use these analyses to show that, across multiple spatial scales, the growth rate of COVID-19 has slowed down with each surge. These results demonstrate a trade-off when estimating growth rates: while spatial aggregation lowers noise, it can increase bias. Public policy and epidemic modelling should be aware of, and aim to address, this distortion.This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.
Highlights
The COVID-19 pandemic has spread rapidly around the globe, claiming millions of lives and wreaking2021 The Authors
We show that spatial aggregation of COVID-19 data across regions leads to higher estimates of growth rates than within most regions—a phenomenon we call aggregation bias
We argue that hot spots and aggregation bias arise because of the heterogeneous growth rate of the disease
Summary
The COVID-19 pandemic has spread rapidly around the globe, claiming millions of lives and wreaking. As a result of aggregation bias, the growth rates of infections and deaths at the state level are higher than for most counties within each state, and the growth rate at the city level overestimates how quickly the disease spreads through most city neighbourhoods. This is further confirmed through the analysis of three major. Spatial aggregation of data produces growth estimates that are systematically higher relative to those within most regions due to these hot spots This offers an explanation for how early epidemics may overestimate the effective reproduction number [9], and demonstrates the trade-off epidemiologists have to make between disaggregation (lower bias) and aggregation (lower noise). Aggregating data could affect parameters in epidemic models and reduce model prediction accuracy
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