Table 1 Policy summary

From: Crowding and the shape of COVID-19 epidemics

Background

There are obvious differences in the geographic distribution of COVID-19 cases within and among countries. We hypothesize that some of these differences are due to spatial variability in population crowding. Using detailed case count data from COVID-19 among cities in China and Italy, we fit multiple regression models to explain variability in the shape of epidemics among them.

Main findings and limitations

We found that cities with higher crowding have longer epidemics and higher attack rates after the first epidemic wave. Using a meta-population model that splits cities into neighborhood subunits is consistent with these findings, suggesting that the hierarchical structure and organization of cities are influential in defining their epidemics. We predict that comparatively rural areas might experience more peaked epidemics. As with all modeling studies, further data generated during the epidemic might change our parameter estimates, and large-scale serological data would help verify our findings. Further, it will be important to evaluate whether cities that have greater peak incidence might be more prone to strained healthcare systems.

Policy implications

Our results have implications for assessing the drivers of transmission of SARS-CoV-2. Spatial factors, such as crowding and population density, might elevate the risk of sustained (longer) outbreaks, even after the implementation of lockdowns. Cities that are less crowded and have lower attack rates might be more susceptible to experiencing future outbreaks if SARS-CoV-2 is successfully re-introduced.

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