New preprint [link] from the Landis Lab showing that likelihood-free deep learning methods estimate phylogenetic rates of viral spread as well classical likelihood-based methods, but in milliseconds instead of hours.
Our team submitted a full grant proposal for our project to the NSF Ecology and Evolution of Infectious Diseases program to seek funding.
Thanks to the Wash U Incubator for Transdisciplinary Futures for selecting our project for support.
The spread of new infectious diseases, climate change, and biodiversity loss are three of the most urgent problems facing society today and they are intricately connected. Exploiting the environment for resources contributes to climate change and biodiversity loss, which both fuel increases in emerging infectious diseases. Many infectious diseases that now threaten humans originated among wildlife, yet we know relatively little about wildlife transmission dynamics and ways to prevent spillover events. Viruses constitute a significant portion of such infectious diseases of global concern, including SARS-CoV-2, Ebola, monkeypox, and Zika. Identifying novel viral pathogens and understanding their transmission dynamics requires advanced genetic sequencing technologies, access to samples from species likely to harbor pathogens of concern to humans, and sophisticated modeling techniques.
Our project focuses on the human-wildlife interface at one site in Uganda, though our innovative framework will inform similar projects wherever humans and wildlife are in close contact. We will apply community-driven methods that meaningfully involve local residents in the research and implementation of strategies to reduce zoonotic disease transmission. We will generate data on known and novel pathogens to reconstruct viral transmission dynamics in wild nonhuman primates and neighboring human communities. Our project will also design and deploy new statistical tools to identify ecosystem-level scenarios that are likely to promote the spread of infectious diseases within and between species. Based on our results, we will use a data-driven and community-centered approach to implement strategies to reduce the opportunities for spillover events, ultimately striving to predict and prevent future pandemics.
Our team is supported by seed funds awarded by the Incubator for Transdisciplinary Futures, a program created through the Arts & Sciences Strategic Plan to stimulate new cross-departmental collaborations throughout Washington University.