Urban Determinants of COVID-19 Spread: a Comparative Study across Three Cities in New York State

Abstract

The ongoing pandemic is laying bare dramatic differences in the spread of COVID-19 across seemingly similar urban environments. Identifying the urban determinants that underlie these differences is an open research question, which can contribute to more epidemiologically resilient cities, optimized testing and detection strategies, and effective immunization efforts. Here, we perform a computational analysis of COVID-19 spread in three cities of similar size in New York State (Colonie, New Rochelle, and Utica) aiming to isolate urban determinants of infections and deaths. We develop detailed digital representations of the cities and simulate COVID-19 spread using a complex agent-based model, taking into account differences in spatial layout, mobility, demographics, and occupational structure of the population. By critically comparing pandemic outcomes across the three cities under equivalent initial conditions, we provide compelling evidence in favor of the central role of hospitals. Specifically, with highly efficacious testing and detection, the number and capacity of hospitals, as well as the extent of vaccination of hospital employees are key determinants of COVID-19 spread. The modulating role of these determinants is reduced at lower efficacy of testing and detection, so that the pandemic outcome becomes equivalent across the three cities. Electronic supplementary material The online version of this article (10.1007/s11524-022-00623-9) contains supplementary material, which is available to authorized users.

Study on the temporal and spatial relationship between public health events and the development of air transport scale: A case of the Southwest China

“…This caused a widespread concern from various fields across various nations or regions [ 1 – 4 ]. A multitude of studies had illustrated that population structure, factors mobility, regional connectivity network [ 5 , 6 ], urban hierarchy [ 7 ], economic resilience and robustness [ 8 , 9 ], digitalization level and development [ 10 , 11 ], as well as the built environment [ 12 , 13 ], all of them were closely related to and interact with the spread of the COVID-19 pandemic. Especially, there are many studies proving that regions with high population density and mobility tended to show a higher difficulty in controlling the epidemic [ 14 – 16 ].…”

The Role of Functional Urban Areas in the Spread of COVID-19 Omicron (Northern Spain)

“…Nevertheless, there is evidence about the role of urban scaling in health outcomes, not only size, but also proximity and mobility among urban areas, as FUAs [ 50 ]. Urban and metropolitan areas are essential in tackling the pandemic, and the spread in dynamic urban areas, such as FUAs, as key areas to analyze the spatial behavior of the virus at regional and local scale [ 51 , 52 ]. In our results, FUAs have positive trends in the upward period of the wave, and the accumulation of positive cases is higher and shorter in time than in rural areas, where the pattern is mainly not statistically significant, and the daily trend in cases is erratic.…”

The impact of urban spatial environment on COVID-19: a case study in Beijing

“…There are also studies that focus on the association between meteorological factors such as air temperature, wind speed, precipitation, and the spatial distribution of COVID-19 cases ( 6 ). Some scholars believed that many demographic factors are significantly correlated with the spread of COVID-19, such as population density and human mobility ( 7 , 8 ). Many studies have demonstrated that the disease was more concentrated in central areas with high population density and dense urban land use ( 9 ).…”