Washington Overdose Model

Interactive policy simulation • Compounding deaths

Live model

ⓘ How it works

BASELINE DATA

1,259 — 2019

Overdose deaths (Year 0)

YEAR 4

3,459 — 2023

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

9 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 9

WITH POLICY CHANGES

4,872

Projected deaths in year 9

LIVES SAVED

8,438

vs. baseline trajectory

BASELINE T_d

2.74 years

time to double (T_d > 0 when V₂/V₁ > 1)

POLICY— t-Double or Half-Life

—

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.

How the Model Works

This interactive simulation is built directly from Washington State overdose death data (2018–2024). It uses the observed exponential compounding rate — deaths roughly doubled every ~2–2.7 years during the surge — and projects forward year by year.

The two sliders reduce this growth rate in targeted ways:

Discourage Migration/Inflow lowers the arrival of new active users into the pool.
Criminalize Use & Distribution reduces lethality and frequency of fatal use among the existing population through stronger enforcement and deterrence.

Move the sliders to see how even modest policy changes bend the curve, slow (or reverse) the compounding dynamic, and potentially save thousands of lives over time. The chart, table, and T_double / half-life metrics update live. This is a simplified illustrative model — real results depend on consistent implementation in an open system — but it clearly shows the power of addressing the two core drivers of the crisis.