Algorithmic Bio-surveillance For Precise Spatio-temporal Prediction of Zoonotic Emergence

TL;DR

This paper presents a machine learning-based bio-surveillance method that analyzes viral protein sequences to predict zoonotic spillovers and species jumps, enabling proactive detection of pandemic risks before outbreaks occur.

Contribution

It introduces a novel approach using genotypic pattern analysis of viral proteins to quantitatively assess and predict zoonotic emergence risk, improving upon reactive surveillance methods.

Findings

Successfully predicted the 2009 swine flu outbreak risk elevation.

Detected early signals of H5N1 emergence from avian reservoirs.

Achieved high accuracy in host tropism classification using global viral sequence data.

Abstract

Viral zoonoses have emerged as the key drivers of recent pandemics. Human infection by zoonotic viruses are either spillover events -- isolated infections that fail to cause a widespread contagion -- or species jumps, where successful adaptation to the new host leads to a pandemic. Despite expensive bio-surveillance efforts, historically emergence response has been reactive, and post-hoc. Here we use machine inference to demonstrate a high accuracy predictive bio-surveillance capability, designed to pro-actively localize an impending species jump via automated interrogation of massive sequence databases of viral proteins. Our results suggest that a jump might not purely be the result of an isolated unfortunate cross-infection localized in space and time; there are subtle yet detectable patterns of genotypic changes accumulating in the global viral population leading up to emergence. Using tens of thousands of protein sequences simultaneously, we train models that track maximum achievable accuracy for disambiguating host tropism from the primary structure of surface proteins, and show that the inverse classification accuracy is a quantitative indicator of jump risk. We validate our claim in the context of the 2009 swine flu outbreak, and the 2004 emergence of H5N1 subspecies of Influenza A from avian reservoirs; illustrating that interrogation of the global viral population can unambiguously track a near monotonic risk elevation over several preceding years leading to eventual emergence.