Host in the Machine - Towards Explainable AI framework which directs experiments to strengthen its ability to answer the question: “Where do viruses hide?”

1^st March 2024, 14:00
Maya Wardeh

Abstract

Most emerging viruses of significant consequence reach humans and domesticated animals via a broad spectrum of wild intermediate and reservoir hosts. However, we have currently only observed ~0.2% of all possible associations between known viruses and hosts, therefore, the vast majority of these potential virus/host associations remain unknown. Hence, our knowledge of host range, reservoirs, and ability to understand and mitigate high consequence viral outbreaks is severely limited.

This talk will showcase how the above problem can be expressed computationally, in ways that both preserve the intricacies of the interplay between the virus biology and host ecology. I will address the inherent biases in our knowledge of the natural world and how they affect our understanding of virus emergence and evolution.

Through multidisciplinary research encompassing computational biology, complex networks, machine learning, and experimental validation, this talk will highlight how we can advance our understanding of viral ecology and inform proactive strategies for disease surveillance and control.