FA-64881 / Epidemic compartment models / Open access
SEIR daily symptom-onset incidence: exposed flow balance · case 01
Total population grows every day and onsets keep rising after the epidemic ends.
ROOT CAUSE
The exposed compartment never loses the individuals that progress to I.
VERIFIED REPAIR
Restore the exposed flow balance rule: `e += exposure - onset`.
Unsuccessful approach: Subtracting removals from E uses the wrong outflow and still breaks conservation.
Case contract
Daily forward-Euler SEIR with sigma=1/latent_days and gamma=1/infectious_days; only I transmits (beta*S*I/pop); seeds start in E and are part of pop; return [daily E->I onsets rounded to 4, total population rounded to 4], or None for non-positive durations or population.
Why this case matters
Compartmental epidemic calculations drive outbreak forecasts, vaccine targets and hospital planning; a single wrong flow, rate conversion or boundary silently changes every downstream number.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(beta, latent_days, infectious_days, pop, e0, days):
if latent_days <= 0 or infectious_days <= 0 or pop <= 0:
return None
sigma = 1.0 / latent_days
gamma = 1.0 / infectious_days
s, e, i, r = float(pop - e0), float(e0), 0.0, 0.0
incidence = []
for day in range(days):
exposure = beta * s * i / pop
onset = sigma * e
removal = gamma * i
s -= exposure
e += exposure
i += onset - removal
r += removal
incidence.append(round(onset, 4))
return [incidence, round(s + e + i + r, 4)]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: influenza-like 2 day latency',
(0.6, 2, 3, 1000, 10, 8),
[[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0]),
('regression: measles-like long latency',
(1.5, 8, 7, 5000, 5, 12),
[[0.625, 0.5469, 0.5956, 0.7239, 0.9186, 1.1836, 1.5328, 1.9882, 2.5798, 3.3471, 4.3413, 5.6281], 5000.0]),
('regression: one-day latency', (0.4, 1, 4, 200, 4, 6), [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0]),
('regression: no transmission',
(0.0, 3, 5, 100, 20, 6),
[[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None)],
[('regression: no transmission',
(0.0, 3, 5, 100, 20, 6),
[[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: fractional latency',
(0.8, 1.5, 2.5, 300, 6, 7),
[[4.0, 1.3333, 2.5351, 2.7755, 3.3698, 3.9741, 4.6871], 300.0]),
('regression: large seed', (0.9, 4, 3, 100, 60, 6), [[15.0, 11.25, 9.7875, 8.9949, 8.2544, 7.4237], 100.0]),
('regression: long infectious period',
(0.3, 5, 14, 2000, 50, 10),
[[10.0, 8.0, 6.985, 6.5977, 6.6203, 6.9212, 7.4223, 8.0781, 8.8635, 9.7655], 2000.0])],
[('regression: influenza-like 2 day latency',
(0.6, 2, 3, 1000, 10, 8),
[[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: fractional latency',
(0.8, 1.5, 2.5, 300, 6, 7),
[[4.0, 1.3333, 2.5351, 2.7755, 3.3698, 3.9741, 4.6871], 300.0]),
('regression: large seed', (0.9, 4, 3, 100, 60, 6), [[15.0, 11.25, 9.7875, 8.9949, 8.2544, 7.4237], 100.0]),
('regression: long infectious period',
(0.3, 5, 14, 2000, 50, 10),
[[10.0, 8.0, 6.985, 6.5977, 6.6203, 6.9212, 7.4223, 8.0781, 8.8635, 9.7655], 2000.0])],
[('regression: influenza-like 2 day latency',
(0.6, 2, 3, 1000, 10, 8),
[[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0]),
('regression: measles-like long latency',
(1.5, 8, 7, 5000, 5, 12),
[[0.625, 0.5469, 0.5956, 0.7239, 0.9186, 1.1836, 1.5328, 1.9882, 2.5798, 3.3471, 4.3413, 5.6281], 5000.0]),
('regression: one-day latency', (0.4, 1, 4, 200, 4, 6), [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: long infectious period',
(0.3, 5, 14, 2000, 50, 10),
[[10.0, 8.0, 6.985, 6.5977, 6.6203, 6.9212, 7.4223, 8.0781, 8.8635, 9.7655], 2000.0])],
[('regression: one-day latency', (0.4, 1, 4, 200, 4, 6), [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0]),
('regression: no transmission',
(0.0, 3, 5, 100, 20, 6),
[[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: fractional latency',
(0.8, 1.5, 2.5, 300, 6, 7),
[[4.0, 1.3333, 2.5351, 2.7755, 3.3698, 3.9741, 4.6871], 300.0]),
('regression: large seed',
(0.9, 4, 3, 100, 60, 6),
[[15.0, 11.25, 9.7875, 8.9949, 8.2544, 7.4237], 100.0])]]
for label, args, expected in fixtures[N - 1]:
check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: influenza-like 2 day latency | [[5.0, 5.0, 6.485, 8.9526, 12.5001, 17.4666, 24.3629, 33.8794], 1113.6466] | [[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0] | Failed |
| regression: measles-like long latency | [[0.625, 0.625, 0.7421, 0.9594, 1.2846, 1.7427, 2.3753, 3.2425, 4.4278, 6.0455, 8.2507, 11.2527], 5041.5733] | [[0.625, 0.5469, 0.5956, 0.7239, 0.9186, 1.1836, 1.5328, 1.9882, 2.5798, 3.3471, 4.3413, 5.6281], 5000.0] | Failed |
| regression: one-day latency | [[4.0, 4.0, 5.568, 8.29, 12.4379, 18.5913], 252.8872] | [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0] | Failed |
| regression: no transmission | [[6.6667, 6.6667, 6.6667, 6.6667, 6.6667, 6.6667], 140.0] | [[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0] | Failed |
| control: boundary zero days | [[], 100.0] | [[], 100.0] | Passed |
| control: invalid zero latency | None | None | Passed |
| control: invalid zero population | None | None | Passed |
SHA-256 / 0c0baa1c71ac3bb9665b1433a8bc6585fcc35fdc1540ab0b92e1fb4daa17e996
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(beta, latent_days, infectious_days, pop, e0, days):
if latent_days <= 0 or infectious_days <= 0 or pop <= 0:
return None
sigma = 1.0 / latent_days
gamma = 1.0 / infectious_days
s, e, i, r = float(pop - e0), float(e0), 0.0, 0.0
incidence = []
for day in range(days):
exposure = beta * s * i / pop
onset = sigma * e
removal = gamma * i
s -= exposure
e += exposure - removal
i += onset - removal
r += removal
incidence.append(round(onset, 4))
return [incidence, round(s + e + i + r, 4)]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: influenza-like 2 day latency',
(0.6, 2, 3, 1000, 10, 8),
[[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0]),
('regression: measles-like long latency',
(1.5, 8, 7, 5000, 5, 12),
[[0.625, 0.5469, 0.5956, 0.7239, 0.9186, 1.1836, 1.5328, 1.9882, 2.5798, 3.3471, 4.3413, 5.6281], 5000.0]),
('regression: one-day latency', (0.4, 1, 4, 200, 4, 6), [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0]),
('regression: no transmission',
(0.0, 3, 5, 100, 20, 6),
[[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None)],
[('regression: no transmission',
(0.0, 3, 5, 100, 20, 6),
[[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: fractional latency',
(0.8, 1.5, 2.5, 300, 6, 7),
[[4.0, 1.3333, 2.5351, 2.7755, 3.3698, 3.9741, 4.6871], 300.0]),
('regression: large seed', (0.9, 4, 3, 100, 60, 6), [[15.0, 11.25, 9.7875, 8.9949, 8.2544, 7.4237], 100.0]),
('regression: long infectious period',
(0.3, 5, 14, 2000, 50, 10),
[[10.0, 8.0, 6.985, 6.5977, 6.6203, 6.9212, 7.4223, 8.0781, 8.8635, 9.7655], 2000.0])],
[('regression: influenza-like 2 day latency',
(0.6, 2, 3, 1000, 10, 8),
[[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: fractional latency',
(0.8, 1.5, 2.5, 300, 6, 7),
[[4.0, 1.3333, 2.5351, 2.7755, 3.3698, 3.9741, 4.6871], 300.0]),
('regression: large seed', (0.9, 4, 3, 100, 60, 6), [[15.0, 11.25, 9.7875, 8.9949, 8.2544, 7.4237], 100.0]),
('regression: long infectious period',
(0.3, 5, 14, 2000, 50, 10),
[[10.0, 8.0, 6.985, 6.5977, 6.6203, 6.9212, 7.4223, 8.0781, 8.8635, 9.7655], 2000.0])],
[('regression: influenza-like 2 day latency',
(0.6, 2, 3, 1000, 10, 8),
[[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0]),
('regression: measles-like long latency',
(1.5, 8, 7, 5000, 5, 12),
[[0.625, 0.5469, 0.5956, 0.7239, 0.9186, 1.1836, 1.5328, 1.9882, 2.5798, 3.3471, 4.3413, 5.6281], 5000.0]),
('regression: one-day latency', (0.4, 1, 4, 200, 4, 6), [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: long infectious period',
(0.3, 5, 14, 2000, 50, 10),
[[10.0, 8.0, 6.985, 6.5977, 6.6203, 6.9212, 7.4223, 8.0781, 8.8635, 9.7655], 2000.0])],
[('regression: one-day latency', (0.4, 1, 4, 200, 4, 6), [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0]),
('regression: no transmission',
(0.0, 3, 5, 100, 20, 6),
[[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: fractional latency',
(0.8, 1.5, 2.5, 300, 6, 7),
[[4.0, 1.3333, 2.5351, 2.7755, 3.3698, 3.9741, 4.6871], 300.0]),
('regression: large seed',
(0.9, 4, 3, 100, 60, 6),
[[15.0, 11.25, 9.7875, 8.9949, 8.2544, 7.4237], 100.0])]]
for label, args, expected in fixtures[N - 1]:
check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: influenza-like 2 day latency | [[5.0, 5.0, 5.6517, 6.7304, 8.1644, 9.9536, 12.131, 14.745], 1032.4806] | [[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0] | Failed |
| regression: measles-like long latency | [[0.625, 0.625, 0.7309, 0.9276, 1.2198, 1.6272, 2.1824, 2.9327, 3.943, 5.3009, 7.1238, 9.5675], 5026.4052] | [[0.625, 0.5469, 0.5956, 0.7239, 0.9186, 1.1836, 1.5328, 1.9882, 2.5798, 3.3471, 4.3413, 5.6281], 5000.0] | Failed |
| regression: one-day latency | [[4.0, 4.0, 4.568, 5.54, 6.85, 8.4744], 220.8701] | [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0] | Failed |
| regression: no transmission | [[6.6667, 6.6667, 6.2222, 5.4222, 4.3674, 3.1621], 118.2272] | [[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0] | Failed |
| control: boundary zero days | [[], 100.0] | [[], 100.0] | Passed |
| control: invalid zero latency | None | None | Passed |
| control: invalid zero population | None | None | Passed |
SHA-256 / 536a7ed062fef71f0ce328da04a7f2dc8c99836617c8da4ceeff0a74fe9443ee
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(beta, latent_days, infectious_days, pop, e0, days):
if latent_days <= 0 or infectious_days <= 0 or pop <= 0:
return None
sigma = 1.0 / latent_days
gamma = 1.0 / infectious_days
s, e, i, r = float(pop - e0), float(e0), 0.0, 0.0
incidence = []
for day in range(days):
exposure = beta * s * i / pop
onset = sigma * e
removal = gamma * i
s -= exposure
e += exposure - onset
i += onset - removal
r += removal
incidence.append(round(onset, 4))
return [incidence, round(s + e + i + r, 4)]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: influenza-like 2 day latency',
(0.6, 2, 3, 1000, 10, 8),
[[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0]),
('regression: measles-like long latency',
(1.5, 8, 7, 5000, 5, 12),
[[0.625, 0.5469, 0.5956, 0.7239, 0.9186, 1.1836, 1.5328, 1.9882, 2.5798, 3.3471, 4.3413, 5.6281], 5000.0]),
('regression: one-day latency', (0.4, 1, 4, 200, 4, 6), [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0]),
('regression: no transmission',
(0.0, 3, 5, 100, 20, 6),
[[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None)],
[('regression: no transmission',
(0.0, 3, 5, 100, 20, 6),
[[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: fractional latency',
(0.8, 1.5, 2.5, 300, 6, 7),
[[4.0, 1.3333, 2.5351, 2.7755, 3.3698, 3.9741, 4.6871], 300.0]),
('regression: large seed', (0.9, 4, 3, 100, 60, 6), [[15.0, 11.25, 9.7875, 8.9949, 8.2544, 7.4237], 100.0]),
('regression: long infectious period',
(0.3, 5, 14, 2000, 50, 10),
[[10.0, 8.0, 6.985, 6.5977, 6.6203, 6.9212, 7.4223, 8.0781, 8.8635, 9.7655], 2000.0])],
[('regression: influenza-like 2 day latency',
(0.6, 2, 3, 1000, 10, 8),
[[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: fractional latency',
(0.8, 1.5, 2.5, 300, 6, 7),
[[4.0, 1.3333, 2.5351, 2.7755, 3.3698, 3.9741, 4.6871], 300.0]),
('regression: large seed', (0.9, 4, 3, 100, 60, 6), [[15.0, 11.25, 9.7875, 8.9949, 8.2544, 7.4237], 100.0]),
('regression: long infectious period',
(0.3, 5, 14, 2000, 50, 10),
[[10.0, 8.0, 6.985, 6.5977, 6.6203, 6.9212, 7.4223, 8.0781, 8.8635, 9.7655], 2000.0])],
[('regression: influenza-like 2 day latency',
(0.6, 2, 3, 1000, 10, 8),
[[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0]),
('regression: measles-like long latency',
(1.5, 8, 7, 5000, 5, 12),
[[0.625, 0.5469, 0.5956, 0.7239, 0.9186, 1.1836, 1.5328, 1.9882, 2.5798, 3.3471, 4.3413, 5.6281], 5000.0]),
('regression: one-day latency', (0.4, 1, 4, 200, 4, 6), [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: long infectious period',
(0.3, 5, 14, 2000, 50, 10),
[[10.0, 8.0, 6.985, 6.5977, 6.6203, 6.9212, 7.4223, 8.0781, 8.8635, 9.7655], 2000.0])],
[('regression: one-day latency', (0.4, 1, 4, 200, 4, 6), [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0]),
('regression: no transmission',
(0.0, 3, 5, 100, 20, 6),
[[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0]),
('control: boundary zero days', (0.5, 3, 5, 100, 5, 0), [[], 100.0]),
('control: invalid zero latency', (0.5, 0, 5, 100, 5, 5), None),
('control: invalid zero population', (0.5, 2, 5, 0, 0, 5), None),
('regression: fractional latency',
(0.8, 1.5, 2.5, 300, 6, 7),
[[4.0, 1.3333, 2.5351, 2.7755, 3.3698, 3.9741, 4.6871], 300.0]),
('regression: large seed',
(0.9, 4, 3, 100, 60, 6),
[[15.0, 11.25, 9.7875, 8.9949, 8.2544, 7.4237], 100.0])]]
for label, args, expected in fixtures[N - 1]:
check(label, solve(*args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: influenza-like 2 day latency | [[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0] | [[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0] | Passed |
| regression: measles-like long latency | [[0.625, 0.5469, 0.5956, 0.7239, 0.9186, 1.1836, 1.5328, 1.9882, 2.5798, 3.3471, 4.3413, 5.6281], 5000.0] | [[0.625, 0.5469, 0.5956, 0.7239, 0.9186, 1.1836, 1.5328, 1.9882, 2.5798, 3.3471, 4.3413, 5.6281], 5000.0] | Passed |
| regression: one-day latency | [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0] | [[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0] | Passed |
| regression: no transmission | [[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0] | [[6.6667, 4.4444, 2.963, 1.9753, 1.3169, 0.8779], 100.0] | Passed |
| control: boundary zero days | [[], 100.0] | [[], 100.0] | Passed |
| control: invalid zero latency | None | None | Passed |
| control: invalid zero population | None | None | Passed |
SHA-256 / e2333974ba5b009d7c4aee4e2387832467a11feea3b30d3810eef5a36d23d6d9
Verification & scope
Deterministic bounded teaching model with a stipulated contract; not a validated scientific or public-health modelling library. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.
Observations recorded using Python 3.12.14 at 2026-09-29T14:47:28.809087+00:00.
Case digest / 091b433bdf43da48bc27162da47c63dc6bc2798805ff101117660ad48858fc74