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Viral zoonoses have emerged as the key drivers of recent pandemics. Humaninfection by zoonotic viruses are either spillover events -- isolatedinfections that fail to cause a widespread contagion -- or species jumps, wheresuccessful adaptation to the new host leads to a pandemic. Despite expensivebio-surveillance efforts, historically emergence response has been reactive,and post-hoc. Here we use machine inference to demonstrate a high accuracypredictive bio-surveillance capability, designed to pro-actively localize animpending species jump via automated interrogation of massive sequencedatabases of viral proteins. Our results suggest that a jump might not purelybe the result of an isolated unfortunate cross-infection localized in space andtime; there are subtle yet detectable patterns of genotypic changesaccumulating in the global viral population leading up to emergence. Using tensof thousands of protein sequences simultaneously, we train models that trackmaximum achievable accuracy for disambiguating host tropism from the primarystructure of surface proteins, and show that the inverse classificationaccuracy is a quantitative indicator of jump risk. We validate our claim in thecontext of the 2009 swine flu outbreak, and the 2004 emergence of H5N1subspecies of Influenza A from avian reservoirs; illustrating thatinterrogation of the global viral population can unambiguously track a nearmonotonic risk elevation over several preceding years leading to eventualemergence.

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