Artificial intelligence is moving from the lab to the front lines of zoonotic-disease surveillance, promising earlier alerts as human–animal interfaces expand. In Uganda, a One Health program is combining livestock and mountain-gorilla health data with EU–Africa project NESTLER to train models that flag brucellosis and Rift Valley fever risks before outbreaks spread to people. Researchers are also using machine learning to sift metagenomic troves for “risky” viruses; one deep-learning tool, LucaProt, surfaced tens of thousands of previously unknown RNA viruses. On the monitoring side, firms like BlueDot fuse multilingual media, public-health feeds and air-travel data to map transmission threats—an approach that anticipated Zika’s spread to Miami—while HealthMap and Boston University’s BEACON layer large language models atop alerts for a more contextual risk picture. The World Bank estimates One Health prevention could cost a fraction of pandemic response, but experts warn the technology isn’t a panacea: models need ground-truth data, political will, and local participation to avoid bias and misuse. The bottom line: AI can sharpen global “radar,” but humans and governance will determine whether alerts translate into action.
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The 100 Days Mission to respond to future pandemics (CEPI)
