I tried to predict disease outbreaks with AI. I failed twice. Here's what I built instead.

The original idea

In early 2025, I had a hypothesis: climate signals and media noise could predict infectious disease outbreaks before official surveillance bodies reported them. If true, even a 2-week advance warning would be enormously valuable for travel risk assessment, supply chains, and public health response.

I pre-registered three backtests before touching the data. Pre-registration matters: it's the only way to distinguish a real finding from one you shaped after seeing the results. The commitment was simple — if the models don't work, I publish the failures.

Experiment 1: Dengue + climate signals. Failed.

Hypothesis: ENSO anomalies, sea surface temperature, and precipitation data could forecast dengue outbreak timing at country level, 4–8 weeks ahead.

Result: the model performed worse than the climatological mean. Climate variables had no predictive skill for dengue at country-level resolution. The signal existed in the literature — but it existed at regional granularity, not at