Title: Algorithmic Bio-surveillance For Precise Spatio-temporal Prediction of Zoonotic Emergence,
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.