Genomic and spatial epidemiology: lessons learned from SARS-CoV-2 pandemic

Purpose of review The SARS-CoV-2 pandemic presented unprecedented challenges, particularly in understanding its complex spatial transmission patterns. The high transmissibility of the virus led to frequent super-spreading events. These events demonstrated clear spatial clustering patterns, often tied to specific events that facilitated transmission. The uneven geographic distribution of medical resources and varying access to care amplified the impact of SARS-CoV-2. Asymptomatic cases further complicated the situation, as infected individuals could silently spread the virus before being identified. Thus, this review examines how genomic and spatial epidemiology approaches can be integrated to answer some of the above-mentioned challenges. We first describe the methodological foundations of genomics and spatial epidemiology, detailing opportunities of their applications during the SARS-CoV-2 pandemic. We then present a novel interdisciplinary framework that combines these approaches to better guide public health interventions. Recent findings During the pandemic, the genomic and spatial approaches were used to address key questions, including “how does the pathogen evolve and diversify?” and “how does the pathogen spread geographically?”. Genomic epidemiology allows researchers to identify viral lineages and new variants. Conversely, spatial epidemiology focused on geographic distribution of infections, analyzing how the virus spread. However, despite their complementary nature, these approaches were largely applied independently during the pandemic. This separation limited our collective ability to fully understand the complex relationships between viral evolution and geographic spread. Summary While phylogeography has traditionally combined phylogenetic and geographic data to understand long-term evolutionary patterns across large areas, events such as the recent SARS-CoV-2 pandemic demand frameworks that can inform public health interventions through joint analysis of genomic and local-scale spatial data.