What we shipped

A proactive next-24h contagion watchlist. The contagion-chain model shipped earlier this week is descriptive: it answers “given country A blocked today, P(B follows in 7d) = X.” This new endpoint flips the question around and runs it live: given the triggers that actually fired in the last 48h, which other countries are most at risk of blocking in the next 24-48h?

This is the difference between “here is a static contagion table” and a watchlist that updates every 6h.

How it works

1. Pull active triggers — any country with a confirmed censorship or mixed incident in the last 48h. Dedup to one trigger per (country, day).
2. Score every candidate follower at horizon = 3 days (the shortest supported horizon — closest proxy for “next 24-48h”). Each follower gets one probability per active trigger.
3. Aggregate via noisy-OR: combined probability that the follower blocks given any active trigger fires is 1 - ∏(1 - p_i). This caps the combined probability at 1.0 and assumes triggers act ~independently.
4. Base-rate-adjust: subtract base_rate_daily × 3 so countries that block every day anyway don’t dominate. Final score is max(0, noisy_or - base_prior_3d).
5. Self-exclude: countries that were themselves triggers in the last 48h are excluded from the watchlist.

Endpoints

• GET /v1/atlas/contagion-watchlist — top-K candidates + active-trigger meta. Optional ?top=N (max 50).
• GET /v1/atlas/contagion-watchlist/info — meta only (small payload for cards / dashboards).
• GET /v1/atlas/contagion-watchlist/{cc} — per-country attribution: which active triggers are pushing this country up the list and by how much.

Example call

curl https://api.voidly.ai/v1/atlas/contagion-watchlist | jq .

Honest caveats (baked into every response)

• Inherits the underlying contagion-chain model’s moderate AUC (~0.67). This is signal, not a deterministic forecast.
• 24-48h is a tight window. Most observed contagion patterns are 3-7d. The endpoint uses horizon = 3 days as the closest available proxy.
• A country on the watchlist does not mean it will block. The score is a relative ranking. Absolute calibration of the underlying pairwise classifier is moderate (Brier ~0.14 at 3d).
• Trigger attribution is probabilistic, not causal. Two countries spiking around the same time can reflect coincidence, shared upstream outages, or regional politics.
• Noisy-OR aggregation assumes triggers act independently, which overestimates risk when multiple triggers share an underlying driver (e.g. a regional election cycle).
• Base-rate adjustment is a heuristic, not a matched counterfactual.
• When zero triggers have fired in the last 48h, the watchlist is empty by design.

Why this matters

Most censorship-forecasting work is either single-country (“Iran 7-day shutdown risk”) or static pairwise tables (“OM → JO is a high-confidence chain”). Neither tells a journalist or operator which 5 countries to watch right now. This watchlist is the connecting layer: it consumes the trigger stream that’s already in the incident pipeline, runs the existing pairwise classifier, and surfaces a small ranked set with per-trigger attribution — all with the same honest-caveats stack the underlying model carries.