Region forecast
Glenmoor
32/ 100 · two-week forward risk32%High(30–34)
EMS naloxone uses
18
naloxone uses
ER overdose visits
12
ER visits
test-strip positivity
14
% positivity
recent losses
1
recent losses
What's driving this forecast
Each indicator's weighted contribution. The heaviest names the driver: EMS naloxone uses.
- ★ EMS naloxone usesweight 0.9 · 36% of forecast
- ER overdose visitsweight 1.1 · 30% of forecast
- test-strip positivityweight 0.8 · 25% of forecast
- recent lossesweight 4 · 9% of forecast
raw = 18×0.9 + 12×1.1 + 14×0.8 + 1×4 → score = min(100, round(raw / 1.4)) = 32
forecast-run.chain
$ no anchored forecast run yetanchor a snapshot from the board to create one
What-if simulator
Adjust aggregate indicators to preview the forecast. Nothing is saved.
32/ 100 forward risk32%High(30–34)
- ★ EMS naloxone usesweight 0.9 · 36% of forecast
- ER overdose visitsweight 1.1 · 30% of forecast
- test-strip positivityweight 0.8 · 25% of forecast
- recent lossesweight 4 · 9% of forecast
Open methodology — every weight in plain sight
A transparent weighted model turns aggregate leading indicators into a 0–100 two-week forward risk score. The heaviest-weighted input names the driver. No person-level data exists anywhere in this pipeline.
| Indicator | Weight | Reference ceiling |
|---|---|---|
| EMS naloxone uses | 0.9 | 60 |
| ER overdose visits | 1.1 | 40 |
| test-strip positivity | 0.8 | 50 |
| recent losses | 4 | 10 |
- score = min(100, round(Σ(value × weight) / 1.4))
- surge flag raised at score ≥ 70
- confidence = 0.6 × data completeness + 0.4 × indicator consistency; sparse or contradictory inputs widen the band (up to ±25) so we never imply false precision