From Viral Evolution to Public Health: Big Data for Outbreak Preparedness
Viruses with rapid mutation rates provide powerful model systems for studying evolutionary processes in near real time. However, viral evolution is not purely stochastic, as trade-offs imposed by protein structure, host-mediated selection, and transmission dynamics limit the space of viable mutations, leading to recurrent and sometimes predictable evolutionary and epidemiological outcomes. The unprecedented availability of viral genomic data has transformed our ability to reconstruct virus evolution, from quantifying the constraints that shape adaptive trajectories to tracking viral spread across time and space.
In this talk, I will outline how my research moves from fundamental evolutionary theory to the analysis of viral spread patterns with applications in genomic epidemiology and outbreak preparedness. By integrating large-scale genomic data with computational modelling, I examine how selection, drift, and biological constraints jointly shape viral diversification and epidemic dynamics. Finally, I will draw on examples from my projects in Mexico, where genomic surveillance and evolutionary inference have helped characterise the introduction, spread, and diversification of multiple viral pathogens. These case studies highlight the importance of shared evolutionary drivers shaping recurrent epidemic emergence and illustrate how theory-informed frameworks can support public health decision-making.