For each analytical capability: what the model actually computes, which data feed it, how it has been validated, and — deliberately — its known limits.
The SSI is a two-stage pipeline. Stage 1 monitors every watched country for the presence of an acute shock and scores it on six weighted factors — scope, criticality, duration, elasticity, interconnectedness, and policy-response capacity. Scope and duration are derived live from the event itself (hazard alert level, conflict status); the remaining four come from per-shock-type analyst priors. Stage 2 projects the economic impact of a detected shock: per-sector severities drive GDP, inflation, fiscal, trade, currency and employment components through sector elasticities and per-country baselines, with cross-sector interaction terms.
GDACS disaster alerts, a verified armed-conflict catalogue, WHO disease-outbreak notifications, and per-country macroeconomic baselines.
Detection is wired end-to-end for two shock types today — natural disasters and geopolitical conflict. Stage-2 projections are being calibrated against documented historical shocks; until that calibration is signed, Stage-2 outputs are presented as projections, not measurements.
For a real earthquake with a published USGS ShakeMap, the engine intersects the actual intensity contours with administrative districts (geoBoundaries ADM2) server-side and reports per-district shaking plus population exposure by intensity band, using GHSL gridded population. For the window before a ShakeMap exists — and for hypothetical scenarios — it synthesises a footprint from an intensity-prediction equation, Allen, Wald & Worden (2012), extended with a finite-fault effective-distance correction built from Wells & Coppersmith (1994) rupture scaling and Thompson & Worden (2018) unknown-strike averaging.
USGS ShakeMap intensity contours and PAGER; GDACS event footprints; geoBoundaries ADM2 districts; GHSL population.
The finite-fault correction was fitted against the published USGS ShakeMap for the M7.1 Venezuela earthquake: mean contour error fell from 21.6 km to 8.8 km, and the corrected model recovers the innermost MMI 8 ring that a point-source form misses. District exposure bucketing was checked against USGS PAGER for the M7.5 Venezuela event: the engine's MMI 6+ exposed population matched PAGER's published figure (8.0M vs 8.0M).
Fatality brackets use the Jaiswal-Wald (2010) lognormal lethality form with a global vulnerability bracket — the span of the range is the uncertainty. They are labeled modeled, order-of-magnitude: a planning signal, never a prediction. When USGS publishes PAGER for an event, PAGER is authoritative — the internal bracket is shown alongside it, never blended with it, never as a competing headline.
At close zoom the globe estimates damage probability per building: Google Open Buildings footprints, the real ShakeMap ground-motion value sampled under each footprint, and a two-part model — a lognormal fragility curve that sets the absolute damage rate of a zone, plus a gradient-boosted ranking that orders buildings within the viewport by observable covariates (footprint size, local density, micro-terrain). Where satellite damage assessments exist, each assessed building's status is displayed as a fact, not a probability — and inside those assessed areas, observed data always wins; the model fills only the gaps between them.
Google Open Buildings footprints; USGS ShakeMap ground-motion grids; commercial very-high-resolution damage assessments (Maxar, BlackSky, Vantor); Copernicus Emergency Management Service rapid-mapping gradings; radar-measured ground deformation (InSAR line-of-sight displacement) as a regional observed layer.
The fragility curve was re-fitted by maximum likelihood on three satellite-observed damage tiles — 46,564 buildings spanning peak ground accelerations of 0.25–0.84 g. The ranking model's honest skill: AUC 0.65–0.80 within an imaged zone, and ≈0.67 when transferred to an unimaged area — above the bare fragility curve's ≈0.58. An earlier coastal-amplification heuristic over-predicted observed damage roughly threefold and was removed: where the observed data contradicted the model, the observed data won.
A per-country nowcast of the 90-day trajectory of a food-insecurity proxy built from near-real-time phone-survey indicators (food-consumption and coping-strategy prevalence, normalised 0–100). The model is a three-part gradient-boosted ensemble exported to ONNX — a baseline regressor, a quantile model for uncertainty bounds, and a non-linear regime model — over 26 features computed from each country's own survey history: lags, rolling means and volatilities, trend, seasonality, and a lean-season flag.
HungerMap LIVE survey history (the only feature source — no media or macro features), plus crop-calendar lean-season timing.
On its held-out test set the model reports MAE 1.6 percentage points, direction accuracy 97.7%, and R² 0.98. Read those honestly: the model is autoregressive — it extrapolates a country's own survey history — so these scores measure trajectory extrapolation, not shock anticipation.
The structural electoral forecaster is a deliberately simple, fully inspectable logistic model over 11 structural features. It scores the probability that a governing party loses its next election, trained on 1,407 competitive national elections across 128 countries since 1990 (competitive defined as V-Dem electoral-democracy ≥ 0.5). It is a calibrated risk rank, not a crystal ball — its mean predicted probability equals the historical base rate (0.544) by construction.
Strictly out-of-sample only: forward-chaining AUC 0.714 (train on the past, predict the next) and leave-one-country-out AUC 0.721 (train on 127 countries, predict the held-out one). No random cross-validation — it leaks.
Every call runs the same loop: predict → lock → resolve → score. A forecast is locked as an immutable snapshot the moment it is made — the number Argus is later graded on can never be retroactively edited. When the outcome is established, the entry is resolved and Brier-scored, misses included. Forecasts built from structural priors alone are flagged as such, and every entry carries its framing — "calibrated relative risk · watch flag" — rendered verbatim, never as "prediction". The track record is public, no sign-in required: /api/ledger/track-record.
For each monitored country, six deterministic danger vectors — conflict, economy, currency, food, governance, regime — are computed from the live signals Argus already scores, plus V-Dem regime type. The convergence count is simply how many vectors are elevated at once. There is no model in this loop: the stacking is arithmetic, so it can be audited by counting.
In a backtest over 4,841 country-years (1996–2024), countries with three or more simultaneously deteriorating vectors were 7.3× more likely to experience an irregular government change the following year than countries with one or none. Risk stacks monotonically with the count.
Argus computes on top of public and licensed datasets, and the boundary of those datasets is the boundary of the product. Outside the monitored-country list, Argus has no live tools — and says so rather than improvising.
On conflict: Argus's conflict signal measures shifts in global media coverage. It is media-derived monitoring — labeled as such — and is not front-line reporting. No claim of ground truth is made where none exists.
Four rules bind every surface of the product. They are enforced in the code paths that produce the numbers, not just promised here.
Every modeled output is stamped as modeled, and order-of-magnitude estimates are labeled as such wherever they appear. A scenario can never be mistaken for an observation.
Wherever a satellite-observed assessment exists, it overrides the model inside its assessed area. Models fill gaps; they do not overrule evidence.
When an authoritative estimate exists — USGS PAGER for earthquake fatalities — it leads. Internal brackets are presented beside it as brackets, never blended into it.
When a feed is down or a value cannot be computed, the surface shows an honest empty state. Nothing is padded, backfilled, or invented to look complete.
Questions about a specific figure, or a limit we have not stated? admin@notamy.app — challenges to the methodology are welcome; they make it better.