{"abstract":"Outcome-known cases include people who fell ill after the observation window.","category":"Epidemic compartment models","checks":7,"contract":"naive CFR = sum(deaths)/sum(cases); delay_pmf[j] is the probability that death occurs j days after onset (j from 0); known = sum_t sum_j cases[t-j]*pmf[j] over t in the series; adjusted = min(sum(deaths)/known, 1); return [naive rounded 6, adjusted rounded 6 or None when known is 0]; None when there are no cases.","evaluation_group":"w2-epidemic-delay-cfr","failed_approach":"Excluding index 0 drops the first day of cases from the known-outcome count.","family":"w2-epidemic-delay-cfr-delay-direction","id":"FA-65246","implementations":{"attempt":{"sha256":"3d89faf406f58d80cf070b7f012395bb66f00f94519ddbf9b1af60ff713a1689","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(cases, deaths, delay_pmf):\n    total_cases = sum(cases)\n    total_deaths = sum(deaths)\n    if total_cases <= 0:\n        return None\n    naive = total_deaths / total_cases\n    known = 0.0\n    for t in range(len(cases)):\n        for j, f in enumerate(delay_pmf):\n            if t - j > 0:\n                known += cases[t - j] * f\n    if known <= 0:\n        return [round(naive, 6), None]\n    adjusted = min(total_deaths / known, 1.0)\n    return [round(naive, 6), round(adjusted, 6)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('regression: stable epidemic',\n   ([50, 50, 50, 50, 50, 50], [0, 1, 2, 2, 2, 2], [0.2, 0.5, 0.3]),\n   [0.03, 0.036735]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667])],\n [('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])],\n [('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('regression: stable epidemic',\n   ([50, 50, 50, 50, 50, 50], [0, 1, 2, 2, 2, 2], [0.2, 0.5, 0.3]),\n   [0.03, 0.036735]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])],\n [('regression: stable epidemic',\n   ([50, 50, 50, 50, 50, 50], [0, 1, 2, 2, 2, 2], [0.2, 0.5, 0.3]),\n   [0.03, 0.036735]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])],\n [('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])]]\nfor label, args, expected in fixtures[N - 1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"9ef738e91728eb2f09f1d5dd631218b2fb4e97c90e98ca61fd5b3dde519c997a","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(cases, deaths, delay_pmf):\n    total_cases = sum(cases)\n    total_deaths = sum(deaths)\n    if total_cases <= 0:\n        return None\n    naive = total_deaths / total_cases\n    known = 0.0\n    for t in range(len(cases)):\n        for j, f in enumerate(delay_pmf):\n            if t + j < len(cases):\n                known += cases[t + j] * f\n    if known <= 0:\n        return [round(naive, 6), None]\n    adjusted = min(total_deaths / known, 1.0)\n    return [round(naive, 6), round(adjusted, 6)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('regression: stable epidemic',\n   ([50, 50, 50, 50, 50, 50], [0, 1, 2, 2, 2, 2], [0.2, 0.5, 0.3]),\n   [0.03, 0.036735]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667])],\n [('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])],\n [('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('regression: stable epidemic',\n   ([50, 50, 50, 50, 50, 50], [0, 1, 2, 2, 2, 2], [0.2, 0.5, 0.3]),\n   [0.03, 0.036735]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])],\n [('regression: stable epidemic',\n   ([50, 50, 50, 50, 50, 50], [0, 1, 2, 2, 2, 2], [0.2, 0.5, 0.3]),\n   [0.03, 0.036735]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])],\n [('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])]]\nfor label, args, expected in fixtures[N - 1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"fixed":{"sha256":"01ee2c9d630e6727e1687eea9652a6ffb68915f863ebe60f5a37a8cf8474002c","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(cases, deaths, delay_pmf):\n    total_cases = sum(cases)\n    total_deaths = sum(deaths)\n    if total_cases <= 0:\n        return None\n    naive = total_deaths / total_cases\n    known = 0.0\n    for t in range(len(cases)):\n        for j, f in enumerate(delay_pmf):\n            if t - j >= 0:\n                known += cases[t - j] * f\n    if known <= 0:\n        return [round(naive, 6), None]\n    adjusted = min(total_deaths / known, 1.0)\n    return [round(naive, 6), round(adjusted, 6)]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('regression: stable epidemic',\n   ([50, 50, 50, 50, 50, 50], [0, 1, 2, 2, 2, 2], [0.2, 0.5, 0.3]),\n   [0.03, 0.036735]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667])],\n [('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])],\n [('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('regression: stable epidemic',\n   ([50, 50, 50, 50, 50, 50], [0, 1, 2, 2, 2, 2], [0.2, 0.5, 0.3]),\n   [0.03, 0.036735]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])],\n [('regression: stable epidemic',\n   ([50, 50, 50, 50, 50, 50], [0, 1, 2, 2, 2, 2], [0.2, 0.5, 0.3]),\n   [0.03, 0.036735]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])],\n [('regression: growing epidemic',\n   ([10, 20, 40, 80, 160], [0, 0, 1, 2, 5], [0.1, 0.3, 0.4, 0.2]),\n   [0.025806, 0.072727]),\n  ('control: long delay no outcomes', ([5, 8, 12], [0, 0, 0], [0, 0, 0, 0, 0, 1]), [0.0, None]),\n  ('control: no cases', ([0, 0, 0], [0, 0, 0], [0.5, 0.5]), None),\n  ('regression: adjusted exceeds one', ([1, 1, 50], [1, 1, 2], [0.0, 0.0, 1.0]), [0.076923, 1.0]),\n  ('regression: immediate deaths', ([20, 30, 25], [2, 3, 2], [1.0]), [0.093333, 0.093333]),\n  ('regression: single day', ([40], [1], [0.6, 0.4]), [0.025, 0.041667]),\n  ('regression: late surge',\n   ([5, 5, 5, 60, 90], [0, 0, 1, 1, 2], [0.05, 0.25, 0.4, 0.3]),\n   [0.024242, 0.111111])]]\nfor label, args, expected in fixtures[N - 1]:\n    check(label, solve(*args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"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.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-epidemic-delay-cfr-delay-direction","generated_at":"2026-09-29T14:47:32.073739+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Compartmental epidemic calculations drive outbreak forecasts, vaccine targets and hospital planning; a single wrong flow, rate conversion or boundary silently changes every downstream number.","repair":"Restore the delay direction rule: `if t - j >= 0: / known += cases[t - j] * f`.","root_cause":"The onset-to-death delay looks forward in time.","sha256":"a034417d4e900b7583f621f20f9e7225aa60bd1f9bb4bbd08d2e6af484eb57af","title":"Delay-adjusted case fatality ratio: delay direction · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":43.263,"exit_code":1,"observations":[{"actual":[0.025806,0.08],"check":"regression: growing epidemic","expected":[0.025806,0.072727],"passed":false},{"actual":[0.03,0.046154],"check":"regression: stable epidemic","expected":[0.03,0.036735],"passed":false},{"actual":[0.0,null],"check":"control: long delay no outcomes","expected":[0.0,null],"passed":true},{"actual":null,"check":"control: no cases","expected":null,"passed":true},{"actual":[0.076923,null],"check":"regression: adjusted exceeds one","expected":[0.076923,1.0],"passed":false},{"actual":[0.093333,0.127273],"check":"regression: immediate deaths","expected":[0.093333,0.093333],"passed":false},{"actual":[0.025,null],"check":"regression: single day","expected":[0.025,0.041667],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: growing epidemic\", \"actual\": [0.025806, 0.08], \"expected\": [0.025806, 0.072727], \"passed\": false}, {\"check\": \"regression: stable epidemic\", \"actual\": [0.03, 0.046154], \"expected\": [0.03, 0.036735], \"passed\": false}, {\"check\": \"control: long delay no outcomes\", \"actual\": [0.0, null], \"expected\": [0.0, null], \"passed\": true}, {\"check\": \"control: no cases\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression: adjusted exceeds one\", \"actual\": [0.076923, null], \"expected\": [0.076923, 1.0], \"passed\": false}, {\"check\": \"regression: immediate deaths\", \"actual\": [0.093333, 0.127273], \"expected\": [0.093333, 0.093333], \"passed\": false}, {\"check\": \"regression: single day\", \"actual\": [0.025, null], \"expected\": [0.025, 0.041667], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":40.505,"exit_code":1,"observations":[{"actual":[0.025806,0.02847],"check":"regression: growing epidemic","expected":[0.025806,0.072727],"passed":false},{"actual":[0.03,0.036735],"check":"regression: stable epidemic","expected":[0.03,0.036735],"passed":true},{"actual":[0.0,null],"check":"control: long delay no outcomes","expected":[0.0,null],"passed":true},{"actual":null,"check":"control: no cases","expected":null,"passed":true},{"actual":[0.076923,0.08],"check":"regression: adjusted exceeds one","expected":[0.076923,1.0],"passed":false},{"actual":[0.093333,0.093333],"check":"regression: immediate deaths","expected":[0.093333,0.093333],"passed":true},{"actual":[0.025,0.041667],"check":"regression: single day","expected":[0.025,0.041667],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: growing epidemic\", \"actual\": [0.025806, 0.02847], \"expected\": [0.025806, 0.072727], \"passed\": false}, {\"check\": \"regression: stable epidemic\", \"actual\": [0.03, 0.036735], \"expected\": [0.03, 0.036735], \"passed\": true}, {\"check\": \"control: long delay no outcomes\", \"actual\": [0.0, null], \"expected\": [0.0, null], \"passed\": true}, {\"check\": \"control: no cases\", \"actual\": null, \"expected\": null, \"passed\": true}, {\"check\": \"regression: adjusted exceeds one\", \"actual\": [0.076923, 0.08], \"expected\": [0.076923, 1.0], \"passed\": false}, {\"check\": \"regression: immediate deaths\", \"actual\": [0.093333, 0.093333], \"expected\": [0.093333, 0.093333], \"passed\": true}, {\"check\": \"regression: single day\", \"actual\": [0.025, 0.041667], \"expected\": [0.025, 0.041667], \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":41.674,"exit_code":0,"observations":[{"actual":[0.025806,0.072727],"check":"regression: growing epidemic","expected":[0.025806,0.072727],"passed":true},{"actual":[0.03,0.036735],"check":"regression: stable epidemic","expected":[0.03,0.036735],"passed":true},{"actual":[0.0,null],"check":"control: long delay no 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