Seattle Overdose Model

Interactive policy simulation • Compounding deaths

Live model
BASELINE DATA
1,259 2019
Overdose deaths (Year 0)
YEAR 4 (2023)
3,459
Observed deaths • ~28.7% CAGR
BASELINE T_d
2.74 years
time to double (V₂/V₁ > 1)

Policy Levers

Discourage Addict Migration / Inflow
Reduce net arrival of active users
65 %
0% (status quo)
100% (strong discouragement)
Criminalize Use & Distribution
Enforcement + deterrence impact
55 %
0% (current approach)
100% (strong enforcement)
Projection horizon
7 years
HOW THE MODEL WORKS
Growth comes from two drivers:
• Inflow/migration of new users
• Lethality + distribution among existing users

Sliders reduce these components. Stronger policies bend the curve downward.
T_d / half-life computed with symmetric ln(2)/ln(ratio) per the equations.
BASELINE (no change)
13,310
Projected deaths in year 7
WITH POLICY CHANGES
4,872
Projected deaths in year 7
LIVES SAVED
8,438
vs. baseline trajectory
BASELINE T_d
2.74 years
time to double (T_d > 0 when V₂/V₁ > 1)
POLICY CHAR. T_d / HALF-LIFE (annual rate)
—
—
Projected Overdose Deaths
Year 0 → selected horizon
Baseline
With policies
Year-by-year projection
Scroll for more years →
Year Baseline With Policies Difference T_d / Half-life
(cumul. from Y0 to row)
This is a simplified exponential model for illustrative purposes. Real-world outcomes depend on many variables including supply, treatment access, and enforcement consistency.
Paid for by Friends of Jillian England (AKA FOJE), PO Box 64, Seattle, WA 98113.
This model is paid for and authorized by the committee to elect Jillian England as defined on the C1.