Genomic Profiling and Spatial SEIR Modeling of COVID-19 Transmission in Western New York

Abstract

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, integrating individual patient-level genomic sequencing data with a spatially informed agent-based disease Susceptible-Exposed-Infectious-Removed (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 population density, movement dynamics, and genetic variations in the viral genomes replicating in New York State (NYS). Our findings shed light on local dynamics of the pandemic, revealing potential hotspots of transmission. Additionally, the genomic analysis provides insights into the genetic heterogeneity of SARS-CoV-2 within a single lineage at a region-specific level. This interdisciplinary approach, bridging genomics and spatial modeling, contributes to a more holistic understanding of COVID-19 dynamics. The results of this study have implications for future public health strategies, guiding targeted interventions and resource allocation to effectively control the spread of similar viruses in the Western New York region.