Leveraging advances in machine learning for the robust classification and interpretation of networks.

The ability to simulate realistic networks based on empirical data is an important task across scientific disciplines, from epidemiology to computer science. Often, simulation approaches involve selecting a suitable network generative model such as Erdös-Rényi or small-world. However, few tools are available to quantify if a particular generative model is suitable for capturing a given network structure or organization.

The spectral underpinnings of pathogen spread on animal networks.

Predicting what factors promote or protect populations from infectious disease is a fundamental epidemiological challenge. Social networks, where nodes represent hosts and edges represent direct or indirect contacts between them, are important in quantifying these aspects of infectious disease dynamics. However, how network structure and epidemic parameters interact in empirical networks to promote or protect animal populations from infectious disease remains a challenge.

Emerging phylogenetic structure of the SARS-CoV-2 pandemic.

Since spilling over into humans, SARS-CoV-2 has rapidly spread across the globe, accumulating significant genetic diversity. The structure of this genetic diversity and whether it reveals epidemiological insights are fundamental questions for understanding the evolutionary trajectory of this virus. Here, we use a recently developed phylodynamic approach to uncover phylogenetic structures underlying the SARS-CoV-2 pandemic.