2026–2030 projections · Open formulas
Where the curve goes if nothing changes
For each metric we have 2020–2025 data on, we fit a simple trend (linear or exponential) and project forward to 2030. Every formula is shown so you can re-run it on your own data, with your own counties, with different smoothing assumptions. The point is the framework, not the prophecy.
The two models we use
Linear trend (default)
Projection(t) = a + b · t
where t = 0 for 2020, t = 1 for 2021, ..., t = 5 for 2025
fit a, b via least squares on 2020-2025 data
plug t = 6 (2026), 7 (2027), ..., 10 (2030)Use this for stable-trending metrics: arrests, jail population, total 911 calls, IDOC releases.
Exponential trend (volatile metrics)
Metric(t) ~ A · e^(k · t)
log-transform to linear:
ln(Metric) = ln(A) + k · t
fit ln(A) and k on 2020-2025
exponentiate for 2026-2030Use this for fast-moving metrics: fentanyl-involved share (rising), overdose deaths in hot-spot counties (post-pandemic spike), naloxone administrations (rising as supply expands).
Cap exponential rises. Fentanyl-involved share follows a logistic curve in real life — it can’t exceed 100%. Apply a soft ceiling at ~85–90%, the asymptote observed in fully-fentanylized national markets, when projecting that metric.
Projected: overdose deaths per 100k (illustrative)
This table illustrates the shape of the projection if 2020–2025 trends continue unchecked. Replace the “input rate” column with your own pulled values from the Indiana Drug Overdose Dashboard for each county to get site-specific projections.
| County | Anchor (2024 est.) | Slope b/yr (illustrative) | 2026 proj. | 2028 proj. | 2030 proj. | Model |
|---|---|---|---|---|---|---|
| Jackson | ~35 / 100k | +0.5 | ~36 | ~37 | ~38 | Linear |
| Scott | ~50 / 100k (high) | -1.0 | ~48 | ~46 | ~44 | Linear (post-IRACS softening) |
| Jennings | ~45 / 100k | +0.2 | ~45 | ~46 | ~46 | Linear |
| Lawrence | ~30 / 100k | -0.4 | ~29 | ~28 | ~27 | Linear |
| Indiana statewide | ~24 / 100k (2024) | -0.5 | ~23 | ~22 | ~21 | Linear (post-2023 decline) |
Slopes shown are illustrative; pull actual 2020–2025 values from the Indiana Drug Overdose Dashboard and re-fit. The 2024 statewide rate of ~24/100k is the documented anchor.
Projected: fentanyl-involved share of overdose deaths
Indiana 2023 baseline: 51% of drug-overdose deaths involved fentanyl. Without a ceiling-cap, exponential extrapolation would put the share above 100% by 2028 — impossible. Use a logistic curve with asymptote at ~88%.
logistic(t) = L / (1 + e^(-k · (t - t0)))
where:
L = 88 (% asymptote)
k = growth-rate parameter (fit from 2020-2025 share)
t0 = inflection year (typically ~2022 for fentanyl)| Year | Statewide IN fentanyl share (proj.) | Model output |
|---|---|---|
| 2023 (anchor) | 51% | data |
| 2026 | ~70% | logistic |
| 2028 | ~80% | logistic |
| 2030 | ~85% | logistic, near asymptote |
Read with caution — if a non-fentanyl synthetic opioid (nitazenes, etc.) becomes dominant, the “fentanyl share” metric stops being meaningful and the underlying “deaths” curve becomes the load-bearing one.
Projected: IDOC releases by county of admission (per 1,000)
IDOC’s “Adult Releases” tables drive this column. Pull 2020–2025 releases from each Jackson-area county, divide by population, fit linear:
releases_per_1k(t) = a + b · t
where t = 0 for 2020, ..., t = 5 for 2025| County | Pop est. | Oct 2024 releases | Annualized rate /1k | 2030 proj. /1k (linear) | Notes |
|---|---|---|---|---|---|
| Jackson | ~46,500 | 6 | ~1.55 | ~1.6 | Stable; matches comparison-set median |
| Scott | ~24,200 | 4 | ~1.98 | ~1.8 | IRACS 9.5% recidivism softens release-flow trend |
| Jennings | ~27,800 | 5 | ~2.16 | ~2.2 | High per-capita rate; also OD hot-spot |
| Bartholomew | ~83,000 | 8 | ~1.16 | ~1.2 | Cummins-anchored employer base |
| Monroe | ~140,300 | 10 | ~0.86 | ~0.9 | Best-positioned (Bloomington / IU) |
| Lawrence | ~45,300 | 5 | ~1.32 | ~1.3 | Bedford metro |
| Clark | ~123,500 | 2 | ~0.19 | ~0.3 | Louisville-cross-river; large pop denominator |
| Floyd | ~81,300 | 1 | ~0.15 | ~0.2 | New Albany |
| Vanderburgh | ~179,000 | 17 | ~1.14 | ~1.2 | Evansville; largest absolute release count |
Annualization assumes Oct 2024 is approximately representative of monthly average (release rate × 12 / pop × 1000). Source: IDOC Oct 2024 Statistical Report Table 24.
Program-participation projection (rehab/reentry/MAT)
For each named program in the Counties — Programs table, track annual clients served. Fit linear growth, sum across counties:
total_program_capacity(t) = sum_over_programs( a_p + b_p · t )Most rural-Indiana SUD and reentry programs are growing slowly (a few percent per year); some are plateauing as funding caps. Pair this projection against the projected overdose curve to surface the treatment gap: how many overdose-death equivalents are absorbed (or not) by the program-side capacity.
Recommended Python implementation
import numpy as np
def fit_linear(years, values):
"""Returns (a, b) where projection(t) = a + b*t, t = year - years[0]."""
t = np.array(years) - years[0]
y = np.array(values, dtype=float)
b, a = np.polyfit(t, y, 1)
return a, b
def fit_exp(years, values):
"""Returns (A, k) where projection(t) = A * exp(k*t)."""
t = np.array(years) - years[0]
y = np.log(np.array(values, dtype=float))
k, lnA = np.polyfit(t, y, 1)
return float(np.exp(lnA)), float(k)
def fit_logistic(years, values, L=88.0):
"""Fit logistic with fixed asymptote L (default 88%) for fentanyl share."""
from scipy.optimize import curve_fit
t = np.array(years) - years[0]
y = np.array(values, dtype=float)
def f(t, k, t0): return L / (1 + np.exp(-k * (t - t0)))
(k, t0), _ = curve_fit(f, t, y, p0=[0.6, 2.0])
return L, k, t0