An integrated eco-evolutionary framework to predict population-level responses of climate-sensitive pathogens

It is critical to gain insight into how climate change impacts evolutionary responses within climate-sensitive pathogen populations, such as increased resilience, opportunistic responses and the emergence of dominant variants from highly variable genomic backgrounds and subsequent global dispersal. This review proposes a framework to support such analysis, by combining genomic evolutionary analysis with climate time-series data in a novel spatiotemporal dataframe for use within machine learning applications, to understand past and future evolutionary pathogen responses to climate change. Recommendations are presented to increase the feasibility of interdisciplinary applications, including the importance of robust spatiotemporal metadata accompanying genome submission to databases. Such workflows will inform accessible public health tools and early-warning systems, to aid decision-making and mitigate future human health threats. [Display omitted] •Climate change drives pathogen ecology, evolution and novel strain emergence.•Absence of tools to identify how pathogens are adapting to climate change.•Combining genomic and climate data will open analysis possibilities.•Machine learning can increase predictive capabilities of future pathogen evolution.