FAILURE MAP
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FA-65086 / Epidemic compartment models / Open access

SIR with daily all-or-nothing vaccination campaign: mixing denominator · case 01

Vaccination makes remaining susceptibles meet infectives more often.

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

ROOT CAUSE

Protected people are removed from the mixing population.

VERIFIED REPAIR

Restore the mixing denominator rule: `inf = beta * s * i / pop`.

Unsuccessful approach: Mixing only among S and I drops both recovered and vaccinated contacts.

Case contract

Each day from day index start_day (inclusive), dose=min(doses_per_day, S) is given to susceptibles before transmission; dose*efficacy moves S->V; failed vaccinees stay in S; mixing uses the full pop; return [S, I, R, V, doses administered] rounded to 3.

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, gamma, pop, i0, doses_per_day, start_day, efficacy, days):
    s, i, r, v = float(pop - i0), float(i0), 0.0, 0.0
    given = 0.0
    for day in range(days):
        if day >= start_day:
            dose = min(doses_per_day, s)
            s -= dose * efficacy
            v += dose * efficacy
            given += dose
        inf = beta * s * i / (pop - v)
        rec = gamma * i
        s -= inf
        i += inf - rec
        r += rec
    return [round(s, 3), round(i, 3), round(r, 3), round(v, 3), round(given, 3)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0])],
 [('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])],
 [('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])],
 [('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])],
 [('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])]]
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: city campaign from day 5[2124.133, 979.149, 2396.718, 4500.0, 5000.0][4007.174, 324.024, 1168.803, 4500.0, 5000.0]Failed
regression: campaign from day 0[503.59, 216.202, 480.208, 800.0, 1000.0][872.113, 79.727, 248.159, 800.0, 1000.0]Failed
regression: supply exceeds susceptibles[0.0, 1.986, 1.455, 96.559, 101.641][0.0, 1.516, 1.233, 97.251, 102.369]Failed
regression: late campaign never starts[777.374, 141.242, 81.385, 0.0, 0.0][777.374, 141.242, 81.385, 0.0, 0.0]Passed
regression: perfect vaccine small town[0.0, 23.996, 102.637, 373.366, 373.366][0.0, 10.271, 55.609, 434.12, 434.12]Failed
regression: poor vaccine[17.099, 320.479, 1441.354, 1221.068, 3052.669][237.471, 307.647, 1134.882, 1320.0, 3300.0]Failed
regression: no doses[896.73, 54.094, 49.176, 0.0, 0.0][896.73, 54.094, 49.176, 0.0, 0.0]Passed

SHA-256 / 1b2c3bb801b37f9cecda689bede48fca8e82558919adcc1b7ef9d3fe4afd3a03

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(beta, gamma, pop, i0, doses_per_day, start_day, efficacy, days):
    s, i, r, v = float(pop - i0), float(i0), 0.0, 0.0
    given = 0.0
    for day in range(days):
        if day >= start_day:
            dose = min(doses_per_day, s)
            s -= dose * efficacy
            v += dose * efficacy
            given += dose
        inf = beta * s * i / (s + i)
        rec = gamma * i
        s -= inf
        i += inf - rec
        r += rec
    return [round(s, 3), round(i, 3), round(r, 3), round(v, 3), round(given, 3)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0])],
 [('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])],
 [('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])],
 [('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])],
 [('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])]]
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: city campaign from day 5[971.697, 1513.883, 3014.42, 4500.0, 5000.0][4007.174, 324.024, 1168.803, 4500.0, 5000.0]Failed
regression: campaign from day 0[281.88, 330.442, 587.679, 800.0, 1000.0][872.113, 79.727, 248.159, 800.0, 1000.0]Failed
regression: supply exceeds susceptibles[0.0, 2.009, 1.463, 96.528, 101.608][0.0, 1.516, 1.233, 97.251, 102.369]Failed
regression: late campaign never starts[762.041, 153.553, 84.406, 0.0, 0.0][777.374, 141.242, 81.385, 0.0, 0.0]Failed
regression: perfect vaccine small town[0.0, 26.46, 111.478, 362.062, 362.062][0.0, 10.271, 55.609, 434.12, 434.12]Failed
regression: poor vaccine[0.173, 332.267, 1634.26, 1033.3, 2583.251][237.471, 307.647, 1134.882, 1320.0, 3300.0]Failed
regression: no doses[893.155, 56.832, 50.012, 0.0, 0.0][896.73, 54.094, 49.176, 0.0, 0.0]Failed

SHA-256 / c47b8c92d898577c4eb91ec5a12694c5eb6c4e41dfa4dbfee68c9fb582691003

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json
import math
N = 1
observations = []
def solve(beta, gamma, pop, i0, doses_per_day, start_day, efficacy, days):
    s, i, r, v = float(pop - i0), float(i0), 0.0, 0.0
    given = 0.0
    for day in range(days):
        if day >= start_day:
            dose = min(doses_per_day, s)
            s -= dose * efficacy
            v += dose * efficacy
            given += dose
        inf = beta * s * i / pop
        rec = gamma * i
        s -= inf
        i += inf - rec
        r += rec
    return [round(s, 3), round(i, 3), round(r, 3), round(v, 3), round(given, 3)]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0])],
 [('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])],
 [('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])],
 [('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: late campaign never starts',
   (0.3, 0.1, 1000, 5, 100, 50, 0.9, 20),
   [777.374, 141.242, 81.385, 0.0, 0.0]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])],
 [('regression: city campaign from day 5',
   (0.4, 0.2, 10000, 20, 200, 5, 0.9, 30),
   [4007.174, 324.024, 1168.803, 4500.0, 5000.0]),
  ('regression: campaign from day 0',
   (0.5, 0.25, 2000, 10, 50, 0, 0.8, 20),
   [872.113, 79.727, 248.159, 800.0, 1000.0]),
  ('regression: supply exceeds susceptibles',
   (0.3, 0.1, 100, 2, 80, 1, 0.95, 6),
   [0.0, 1.516, 1.233, 97.251, 102.369]),
  ('regression: perfect vaccine small town',
   (0.6, 0.2, 500, 5, 40, 2, 1.0, 15),
   [0.0, 10.271, 55.609, 434.12, 434.12]),
  ('regression: poor vaccine',
   (0.45, 0.15, 3000, 30, 150, 3, 0.4, 25),
   [237.471, 307.647, 1134.882, 1320.0, 3300.0]),
  ('regression: no doses', (0.4, 0.2, 1000, 10, 0, 0, 0.9, 10), [896.73, 54.094, 49.176, 0.0, 0.0]),
  ('regression: start day one', (0.35, 0.1, 800, 4, 30, 1, 0.7, 12), [519.85, 32.463, 16.687, 231.0, 330.0])]]
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: city campaign from day 5[4007.174, 324.024, 1168.803, 4500.0, 5000.0][4007.174, 324.024, 1168.803, 4500.0, 5000.0]Passed
regression: campaign from day 0[872.113, 79.727, 248.159, 800.0, 1000.0][872.113, 79.727, 248.159, 800.0, 1000.0]Passed
regression: supply exceeds susceptibles[0.0, 1.516, 1.233, 97.251, 102.369][0.0, 1.516, 1.233, 97.251, 102.369]Passed
regression: late campaign never starts[777.374, 141.242, 81.385, 0.0, 0.0][777.374, 141.242, 81.385, 0.0, 0.0]Passed
regression: perfect vaccine small town[0.0, 10.271, 55.609, 434.12, 434.12][0.0, 10.271, 55.609, 434.12, 434.12]Passed
regression: poor vaccine[237.471, 307.647, 1134.882, 1320.0, 3300.0][237.471, 307.647, 1134.882, 1320.0, 3300.0]Passed
regression: no doses[896.73, 54.094, 49.176, 0.0, 0.0][896.73, 54.094, 49.176, 0.0, 0.0]Passed

SHA-256 / 64a5d86d49a20a9e4462ceff0a76c6fe0784a8d00b2f51369206249848577086

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 2026-09-29T14:47:30.733456+00:00.

Case digest / 965a69ff6fcb2c895fd28e16ee9efc15bc4f1c566f28f8973d8135ac02a818e5