{"abstract":"Imperfect vaccines appear to need lower coverage than perfect ones.","category":"Epidemic compartment models","checks":7,"contract":"R0 = beta*sigma/((sigma+mu)*(gamma+mu)); when R0<=1 return [R0,0.0,0.0]; herd threshold 1-1/R0; critical coverage threshold/efficacy, None if it exceeds 1 (exactly 1 is feasible); values rounded to 6; None for invalid rates or efficacy outside (0,1].","evaluation_group":"w2-epidemic-seir-r0-coverage","failed_approach":"Adding the failure fraction is not the all-or-nothing coverage adjustment.","family":"w2-epidemic-seir-r0-coverage-efficacy-adjustment","id":"FA-64916","implementations":{"attempt":{"sha256":"0bbad1f473f1d49627f06191e16db0fde37cb40b1be315cdbb7a1dfcc6e77b16","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(beta, sigma, gamma, mu, efficacy):\n    if min(beta, sigma, gamma) <= 0 or mu < 0 or not 0 < efficacy <= 1:\n        return None\n    r0 = beta * sigma / ((sigma + mu) * (gamma + mu))\n    if r0 <= 1:\n        return [round(r0, 6), 0.0, 0.0]\n    threshold = 1 - 1 / r0\n    coverage = threshold + (1 - efficacy)\n    if coverage > 1:\n        coverage = None\n    else:\n        coverage = round(coverage, 6)\n    return [round(r0, 6), round(threshold, 6), coverage]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('regression: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('regression: infeasible coverage poor vaccine', (1.2, 0.3, 0.2, 0.01, 0.5), [5.529954, 0.819167, None]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6])],\n [('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('regression: infeasible coverage poor vaccine', (1.2, 0.3, 0.2, 0.01, 0.5), [5.529954, 0.819167, None]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6]),\n  ('control: invalid efficacy zero', (1.0, 0.5, 0.2, 0.0, 0.0), None),\n  ('control: invalid negative mortality', (1.0, 0.5, 0.2, -0.1, 0.9), None),\n  ('regression: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('regression: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6]),\n  ('control: invalid efficacy zero', (1.0, 0.5, 0.2, 0.0, 0.0), None),\n  ('control: invalid negative mortality', (1.0, 0.5, 0.2, -0.1, 0.9), None),\n  ('control: perfect vaccine', (0.9, 0.25, 0.2, 0.001, 1.0), [4.459773, 0.775773, 0.775773]),\n  ('regression: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('regression: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('regression: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: perfect vaccine', (0.9, 0.25, 0.2, 0.001, 1.0), [4.459773, 0.775773, 0.775773]),\n  ('regression: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('regression: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('regression: infeasible coverage poor vaccine', (1.2, 0.3, 0.2, 0.01, 0.5), [5.529954, 0.819167, None]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6]),\n  ('control: invalid efficacy zero', (1.0, 0.5, 0.2, 0.0, 0.0), None)]]\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":"fc64e728ac27e44b6efe6cf3718e96568386ad7811255f578ad1601e66e05927","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(beta, sigma, gamma, mu, efficacy):\n    if min(beta, sigma, gamma) <= 0 or mu < 0 or not 0 < efficacy <= 1:\n        return None\n    r0 = beta * sigma / ((sigma + mu) * (gamma + mu))\n    if r0 <= 1:\n        return [round(r0, 6), 0.0, 0.0]\n    threshold = 1 - 1 / r0\n    coverage = threshold * efficacy\n    if coverage > 1:\n        coverage = None\n    else:\n        coverage = round(coverage, 6)\n    return [round(r0, 6), round(threshold, 6), coverage]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('regression: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('regression: infeasible coverage poor vaccine', (1.2, 0.3, 0.2, 0.01, 0.5), [5.529954, 0.819167, None]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6])],\n [('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('regression: infeasible coverage poor vaccine', (1.2, 0.3, 0.2, 0.01, 0.5), [5.529954, 0.819167, None]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6]),\n  ('control: invalid efficacy zero', (1.0, 0.5, 0.2, 0.0, 0.0), None),\n  ('control: invalid negative mortality', (1.0, 0.5, 0.2, -0.1, 0.9), None),\n  ('regression: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('regression: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6]),\n  ('control: invalid efficacy zero', (1.0, 0.5, 0.2, 0.0, 0.0), None),\n  ('control: invalid negative mortality', (1.0, 0.5, 0.2, -0.1, 0.9), None),\n  ('control: perfect vaccine', (0.9, 0.25, 0.2, 0.001, 1.0), [4.459773, 0.775773, 0.775773]),\n  ('regression: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('regression: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('regression: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: perfect vaccine', (0.9, 0.25, 0.2, 0.001, 1.0), [4.459773, 0.775773, 0.775773]),\n  ('regression: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('regression: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('regression: infeasible coverage poor vaccine', (1.2, 0.3, 0.2, 0.01, 0.5), [5.529954, 0.819167, None]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6]),\n  ('control: invalid efficacy zero', (1.0, 0.5, 0.2, 0.0, 0.0), None)]]\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":"4f349110a3c4254caadf5c2110ca95ae8900fbbf22bfb841be8c69cd7eafe066","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nimport math\nN = 1\nobservations = []\ndef solve(beta, sigma, gamma, mu, efficacy):\n    if min(beta, sigma, gamma) <= 0 or mu < 0 or not 0 < efficacy <= 1:\n        return None\n    r0 = beta * sigma / ((sigma + mu) * (gamma + mu))\n    if r0 <= 1:\n        return [round(r0, 6), 0.0, 0.0]\n    threshold = 1 - 1 / r0\n    coverage = threshold / efficacy\n    if coverage > 1:\n        coverage = None\n    else:\n        coverage = round(coverage, 6)\n    return [round(r0, 6), round(threshold, 6), coverage]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('regression: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('regression: infeasible coverage poor vaccine', (1.2, 0.3, 0.2, 0.01, 0.5), [5.529954, 0.819167, None]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6])],\n [('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('regression: infeasible coverage poor vaccine', (1.2, 0.3, 0.2, 0.01, 0.5), [5.529954, 0.819167, None]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6]),\n  ('control: invalid efficacy zero', (1.0, 0.5, 0.2, 0.0, 0.0), None),\n  ('control: invalid negative mortality', (1.0, 0.5, 0.2, -0.1, 0.9), None),\n  ('regression: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('regression: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6]),\n  ('control: invalid efficacy zero', (1.0, 0.5, 0.2, 0.0, 0.0), None),\n  ('control: invalid negative mortality', (1.0, 0.5, 0.2, -0.1, 0.9), None),\n  ('control: perfect vaccine', (0.9, 0.25, 0.2, 0.001, 1.0), [4.459773, 0.775773, 0.775773]),\n  ('regression: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('regression: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('regression: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: perfect vaccine', (0.9, 0.25, 0.2, 0.001, 1.0), [4.459773, 0.775773, 0.775773]),\n  ('regression: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('regression: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('regression: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('control: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('control: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('regression: infeasible coverage poor vaccine', (1.2, 0.3, 0.2, 0.01, 0.5), [5.529954, 0.819167, None]),\n  ('control: high mortality relative to latency', (3.0, 0.1, 0.5, 0.1, 1.0), [2.5, 0.6, 0.6]),\n  ('control: invalid efficacy zero', (1.0, 0.5, 0.2, 0.0, 0.0), None)]]\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-seir-r0-coverage-efficacy-adjustment","generated_at":"2026-09-29T14:47:29.216689+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 efficacy adjustment rule: `coverage = threshold / efficacy`.","root_cause":"Coverage multiplies by efficacy instead of dividing by it.","sha256":"9f3145d6132e20e9c8b20ebec626eb4ea09baa0a09cd0a8de3726db23286846f","title":"SEIR R0 with vital dynamics and vaccine coverage: efficacy adjustment · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":38.408,"exit_code":1,"observations":[{"actual":[14.4,0.930556,0.980556],"check":"regression: measles-like high R0","expected":[14.4,0.930556,0.979532],"passed":false},{"actual":[1.780627,0.4384,0.8384],"check":"regression: flu-like moderate R0 with births","expected":[1.780627,0.4384,0.730667],"passed":false},{"actual":[2.0,0.5,1.0],"check":"regression: exact R0 of two with half efficacy","expected":[2.0,0.5,1.0],"passed":true},{"actual":[0.8,0.0,0.0],"check":"control: subcritical with mortality","expected":[0.8,0.0,0.0],"passed":true},{"actual":[1.0,0.0,0.0],"check":"control: boundary R0 exactly one","expected":[1.0,0.0,0.0],"passed":true},{"actual":[5.529954,0.819167,null],"check":"regression: infeasible coverage poor vaccine","expected":[5.529954,0.819167,null],"passed":true},{"actual":[2.5,0.6,0.6],"check":"control: high mortality relative to latency","expected":[2.5,0.6,0.6],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: measles-like high R0\", \"actual\": [14.4, 0.930556, 0.980556], \"expected\": [14.4, 0.930556, 0.979532], \"passed\": false}, {\"check\": \"regression: flu-like moderate R0 with births\", \"actual\": [1.780627, 0.4384, 0.8384], \"expected\": [1.780627, 0.4384, 0.730667], \"passed\": false}, {\"check\": \"regression: exact R0 of two with half efficacy\", \"actual\": [2.0, 0.5, 1.0], \"expected\": [2.0, 0.5, 1.0], \"passed\": true}, {\"check\": \"control: subcritical with mortality\", \"actual\": [0.8, 0.0, 0.0], \"expected\": [0.8, 0.0, 0.0], \"passed\": true}, {\"check\": \"control: boundary R0 exactly one\", \"actual\": [1.0, 0.0, 0.0], \"expected\": [1.0, 0.0, 0.0], \"passed\": true}, {\"check\": \"regression: infeasible coverage poor vaccine\", \"actual\": [5.529954, 0.819167, null], \"expected\": [5.529954, 0.819167, null], \"passed\": true}, {\"check\": \"control: high mortality relative to latency\", \"actual\": [2.5, 0.6, 0.6], \"expected\": [2.5, 0.6, 0.6], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":43.48,"exit_code":1,"observations":[{"actual":[14.4,0.930556,0.884028],"check":"regression: measles-like high R0","expected":[14.4,0.930556,0.979532],"passed":false},{"actual":[1.780627,0.4384,0.26304],"check":"regression: flu-like moderate R0 with births","expected":[1.780627,0.4384,0.730667],"passed":false},{"actual":[2.0,0.5,0.25],"check":"regression: exact R0 of two with half efficacy","expected":[2.0,0.5,1.0],"passed":false},{"actual":[0.8,0.0,0.0],"check":"control: subcritical with mortality","expected":[0.8,0.0,0.0],"passed":true},{"actual":[1.0,0.0,0.0],"check":"control: boundary R0 exactly one","expected":[1.0,0.0,0.0],"passed":true},{"actual":[5.529954,0.819167,0.409583],"check":"regression: infeasible coverage poor vaccine","expected":[5.529954,0.819167,null],"passed":false},{"actual":[2.5,0.6,0.6],"check":"control: high mortality relative to latency","expected":[2.5,0.6,0.6],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: measles-like high R0\", \"actual\": [14.4, 0.930556, 0.884028], \"expected\": [14.4, 0.930556, 0.979532], \"passed\": false}, {\"check\": \"regression: flu-like moderate R0 with births\", \"actual\": [1.780627, 0.4384, 0.26304], \"expected\": [1.780627, 0.4384, 0.730667], \"passed\": false}, {\"check\": \"regression: exact R0 of two with half efficacy\", \"actual\": [2.0, 0.5, 0.25], \"expected\": [2.0, 0.5, 1.0], \"passed\": false}, {\"check\": \"control: subcritical with mortality\", \"actual\": [0.8, 0.0, 0.0], \"expected\": [0.8, 0.0, 0.0], \"passed\": true}, {\"check\": \"control: boundary R0 exactly one\", \"actual\": [1.0, 0.0, 0.0], \"expected\": [1.0, 0.0, 0.0], \"passed\": true}, {\"check\": \"regression: infeasible 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