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

Area of Science:

• Evolutionary biology
• Climate science
• Genomics
• Machine learning
• Public health

Background:

• Climate change significantly impacts pathogen populations, influencing their evolutionary trajectory.
• Understanding these evolutionary responses, including increased resilience and variant emergence, is crucial for public health.
• Pathogen dispersal is often facilitated by genomic variability and environmental factors.

Purpose of the Study:

• To propose a novel framework for analyzing pathogen evolutionary responses to climate change.
• To integrate genomic evolutionary analysis with climate time-series data for machine learning applications.
• To enhance understanding of past and future pathogen evolution under climate change scenarios.

Main Methods:

• Developing a spatiotemporal dataframe combining genomic data and climate time-series.
• Utilizing machine learning applications to analyze the integrated data.
• Reviewing and recommending best practices for data submission and interdisciplinary collaboration.

Main Results:

• The proposed framework enables a comprehensive analysis of pathogen evolutionary dynamics in response to climate change.
• Machine learning applications can effectively model and predict pathogen evolutionary trajectories.
• Emphasis on robust spatiotemporal metadata is key for accurate analysis.

Conclusions:

• The integrated framework provides a powerful tool for understanding pathogen evolution and climate change interactions.
• This approach can inform the development of public health tools and early-warning systems.
• Mitigating future human health threats requires interdisciplinary collaboration and advanced analytical methods.