COVID-19 attack rate increases with city size (2003.10376v2)

Abstract: The current outbreak of novel coronavirus disease 2019 (COVID-19) poses an unprecedented global health and economic threat to interconnected human societies. Until a vaccine is developed, strategies for controlling the outbreak rely on aggressive social distancing. These measures largely disconnect the social network fabric of human societies, especially in urban areas. Here, we estimate the growth rates and reproductive numbers of COVID-19 in US cities from March 14th through March 19th to reveal a power-law scaling relationship to city population size. This means that COVID-19 is spreading faster on average in larger cities with the additional implication that, in an uncontrolled outbreak, larger fractions of the population are expected to become infected in more populous urban areas. We discuss the implications of these observations for controlling the COVID-19 outbreak, emphasizing the need to implement more aggressive distancing policies in larger cities while also preserving socioeconomic activity.

Analysis of COVID-19 Attack Rates in Relation to Urban Size

The paper titled "COVID-19 attack rate increases with city size," authored by Andrew J. Stier, Marc G. Berman, and Luis M. A. Bettencourt, provides an empirical investigation into the transmission dynamics of the COVID-19 pandemic in the context of urban environments. The paper examines the growth rates and reproductive numbers of COVID-19 across various US cities, uncovering a significant scaling law related to population size.

Key Findings

The research identifies a power-law relationship between the speed of COVID-19 spread, quantified by the reproductive number $R$, and city population size. This finding indicates that larger cities tend to experience faster transmission rates, emphasizing the higher risk posed by urban network density in spreading infectious diseases. The authors estimate reproductive numbers spanning the range $R=2.2-6.5$, considerably higher than those reported for seasonal influenza, suggesting robust epidemic potential in densely populated areas.

Empirical results demonstrate that COVID-19 cases grow approximately 2.4 times faster in larger metropolises such as New York-Newark-Jersey compared to smaller locales like Oak Harbour, WA. These differences in growth rates imply that without stringent interventions, larger cities could witness more extensive outbreaks, affecting a larger fraction of their populations.

Implications

The paper's implications are manifold, primarily focusing on the need for city-specific public health policies. In larger cities, public health strategies must be more aggressive to reduce $R$ below the epidemic threshold, thereby halting disease transmission. The paper argues for differentiated social distancing measures, suggesting a more nuanced approach based on urban population density.

Additionally, this research supports the consideration of city size when designing interventions to contain pandemics. Since larger cities exhibit a higher degree of socioeconomic connectivity, they are not only more susceptible to rapid disease spread but can also act as reservoirs, potentially instigating secondary outbreaks.

Theoretical Framework and Future Research

The authors provide a quantitative framework using a Susceptible-Infected-Recovered (SIR) model to explore the implications of their empirical findings. The discussion pivots on the network theory where socioeconomic interactions scale with city size, making $R$ a city-size dependent parameter.

Future research directions include the examination of intra-city variations where individual neighborhoods or sectors might show distinct infection dynamics. Further exploration is also needed on how technology-driven social interactions, which maintain connectivity while limiting physical contact, could mitigate disease spread without severely affecting economic activities.

Conclusion

The paper makes a compelling case for the critical role of urban scaling in pandemic dynamics, highlighting the complexities associated with managing infectious diseases in large urban areas. It calls for sophisticated, data-driven strategies that acknowledge the heterogeneity of urban networks, directing efforts towards not only controlling disease transmission but also preserving the underlying socioeconomic structures essential for recovery and resilience. This research sets a precedent for integrating urban studies with epidemiological models, aiming to create resilient urban environments in the face of global health threats.