FAILURE MAP
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FA-64876 / Epidemic compartment models / Open access

SEIR daily symptom-onset incidence: incidence definition · case 01

The reported onset curve is shifted a latent period earlier and starts at zero.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

New infections S->E are reported instead of symptom onsets E->I.

VERIFIED REPAIR

Restore the incidence definition rule: `incidence.append(round(onset, 4))`.

Unsuccessful approach: Reporting the net change of I subtracts removals and can be negative.

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 - onset
        i += onset - removal
        r += removal
        incidence.append(round(exposure, 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 fixtureActualExpectedOutcome
regression: influenza-like 2 day latency[[0.0, 2.97, 3.4546, 3.9091, 4.4148, 4.9791, 5.6069, 6.3028], 1000.0][[5.0, 2.5, 2.735, 3.0948, 3.5019, 3.9584, 4.4687, 5.0378], 1000.0]Failed
regression: measles-like long latency[[0.0, 0.9366, 1.622, 2.2818, 3.0386, 3.9774, 5.1759, 6.721, 8.7183, 11.3004, 14.6357, 18.9372], 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]Failed
regression: one-day latency[[0.0, 1.568, 1.1666, 1.4758, 1.5459, 1.7116], 200.0][[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0]Failed
regression: no transmission[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 100.0][[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 latencyNoneNonePassed
control: invalid zero populationNoneNonePassed

SHA-256 / 205dfecdece9ae1479ce69e719bfec1e9666d4ecdd614754195890db51f24019

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 - onset
        i += onset - removal
        r += removal
        incidence.append(round(onset - removal, 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 fixtureActualExpectedOutcome
regression: influenza-like 2 day latency[[5.0, 0.8333, 0.7906, 0.8868, 0.9984, 1.122, 1.2584, 1.408], 1000.0][[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, 0.4576, 0.4409, 0.5062, 0.6287, 0.8039, 1.0382, 1.3453, 1.7447, 2.2628, 2.9337, 3.8014], 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]Failed
regression: one-day latency[[4.0, -1.0, 0.818, 0.2121, 0.4683, 0.4213], 200.0][[4.0, 0.0, 1.568, 1.1666, 1.4758, 1.5459], 200.0]Failed
regression: no transmission[[6.6667, 3.1111, 1.0074, -0.1817, -0.8038, -1.082], 100.0][[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 latencyNoneNonePassed
control: invalid zero populationNoneNonePassed

SHA-256 / 44a031b165a8e5432ea18085366844812735ab233ee1041c4fa43f35bd018f61

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 fixtureActualExpectedOutcome
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 latencyNoneNonePassed
control: invalid zero populationNoneNonePassed

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 2026-09-29T14:47:28.805400+00:00.

Case digest / ccb2435f80bb1c2ab2de33b4e35e2a43d44207eb2dde4a7ffcc691edc92d3442