This graph shows the population density in the federal state of New York from 1960 to 2018. In 2018, the population density of New York stood at 414.7 residents per square mile of land area.

The COVID-19 pandemic has prompted an unprecedented global effort to understand and mitigate the spread of the SARS-CoV-2 virus. In this study, we present a comprehensive analysis of COVID-19 in Western New York (WNY), integrating individual patient-level genomic sequencing data with a spatially informed agent-based disease Susceptible-Exposed-Infectious-Recovered (SEIR) computational model. The integration of genomic and spatial data enables a multi-faceted exploration of the factors influencing the transmission patterns of COVID-19, including genetic variations in the viral genomes, population density, and movement dynamics in New York State (NYS). Our genomic analyses provide insights into the genetic heterogeneity of SARS-CoV-2 within a single lineage, at region-specific resolutions, while our population analyses provide models for SARS-CoV-2 lineage transmission. Together, our findings shed light on localized dynamics of the pandemic, revealing potential cross-county transmission networks. This interdisciplinary approach, bridging genomics and spatial modeling, contributes to a more comprehensive understanding of COVID-19 dynamics. The results of this study have implications for future public health strategies, including guiding targeted interventions and resource allocations to control the spread of similar viruses.

Background: Cervical artery dissection (CeAD) is a leading cause of stroke in young adults. Incidence estimates may be limited by under- or overdiagnosis. Objective: We aimed to investigate if CeAD diagnosis would be higher in urban centers compared to rural regions of New York State (NYS). Methods: For this ecological study, administrative codes were used to identify CeAD discharges in the NYS Statewide Planning and Research Cooperative System (SPARCS) from 2009 to 2014. Rural Urban Commuting Area (RUCA) codes were taken from the US Department of Agriculture and included the classifications metropolitan, micropolitan, small town, and rural. Negative binomial models were used to calculate effect estimates and 95% confidence limits (eβ; 95% CL) for the association between RUCA classification and the number of dissections per ZIP code. Models were further adjusted by population. Results: Population information was obtained from the US Census Bureau on 1,797 NYS ZIP codes (70.7% of NYS ZIP codes), 826 of which had at least 1 CeAD-related discharge from 2009 to 2014. Nonrural ZIP codes were more likely to report more CeAD cases relative to rural areas even after adjusting for population (metropolitan effect = eβ 5.00; 95% CI: 3.75–6.66; micropolitan effect 3.02; 95% CI: 2.16–4.23; small town effect 2.34; 95% CI: 1.58–3.47). Conclusions: CeAD diagnosis correlates with population density as defined by rural-urban status. Our results could be due to underdiagnosis in rural areas or overdiagnosis with increasing urbanicity.