{"abstract":"Subcritical pathogens report a negative herd-immunity threshold and negative coverage.","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":"Reporting coverage None for a subcritical pathogen conflates \"no vaccination needed\" with \"infeasible\".","family":"w2-epidemic-seir-r0-coverage-subcritical-branch","id":"FA-64906","implementations":{"attempt":{"sha256":"e847505443d43a872c7f93c4b083934cc1871c1709fba64a6f6710fbd8aaca02","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, None]\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 = [[('control: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('control: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: 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 [('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: 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  ('control: perfect vaccine', (0.9, 0.25, 0.2, 0.001, 1.0), [4.459773, 0.775773, 0.775773])],\n [('control: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\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  ('control: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('control: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('control: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: 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  ('control: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('control: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: 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":"591fe4a511ea139edcfda2dfde2c7c29523b7d01b4970e7208f5f9e9c691f4ba","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    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 = [[('control: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('control: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: 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 [('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: 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  ('control: perfect vaccine', (0.9, 0.25, 0.2, 0.001, 1.0), [4.459773, 0.775773, 0.775773])],\n [('control: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\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  ('control: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('control: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('control: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: 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  ('control: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('control: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: 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":"4a4e788f32b6a539cdd601af20b6d171c44d811b6f6488c71241c2ef13021e9e","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 = [[('control: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('control: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: 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 [('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: 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  ('control: perfect vaccine', (0.9, 0.25, 0.2, 0.001, 1.0), [4.459773, 0.775773, 0.775773])],\n [('control: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\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  ('control: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('control: measles-like high R0', (1.8, 0.1, 0.125, 0.0, 0.95), [14.4, 0.930556, 0.979532]),\n  ('control: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: 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  ('control: long latency high mortality', (2.4, 0.05, 0.3, 0.04, 0.85), [3.921569, 0.745, 0.876471])],\n [('control: flu-like moderate R0 with births', (0.5, 0.5, 0.25, 0.02, 0.6), [1.780627, 0.4384, 0.730667]),\n  ('control: exact R0 of two with half efficacy', (2.0, 1.0, 1.0, 0.0, 0.5), [2.0, 0.5, 1.0]),\n  ('regression: subcritical with mortality', (0.3, 0.2, 0.25, 0.05, 0.9), [0.8, 0.0, 0.0]),\n  ('regression: boundary R0 exactly one', (1.0, 1.0, 1.0, 0.0, 0.8), [1.0, 0.0, 0.0]),\n  ('control: 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-subcritical-branch","generated_at":"2026-09-29T14:47:28.941310+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 subcritical branch rule: `if r0 <= 1: / return [round(r0, 6), 0.0, 0.0]`.","root_cause":"The R0<=1 early return is missing, so 1-1/R0 is evaluated below one.","sha256":"2c3b8397ee2390afa4142ecf4d4a0fa6dff68beb38626c2f018962e68266a149","title":"SEIR R0 with vital dynamics and vaccine coverage: subcritical branch · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":43.611,"exit_code":1,"observations":[{"actual":[14.4,0.930556,0.979532],"check":"control: measles-like high R0","expected":[14.4,0.930556,0.979532],"passed":true},{"actual":[1.780627,0.4384,0.730667],"check":"control: flu-like moderate R0 with births","expected":[1.780627,0.4384,0.730667],"passed":true},{"actual":[2.0,0.5,1.0],"check":"control: exact R0 of two with half efficacy","expected":[2.0,0.5,1.0],"passed":true},{"actual":[0.8,0.0,null],"check":"regression: subcritical with mortality","expected":[0.8,0.0,0.0],"passed":false},{"actual":[1.0,0.0,null],"check":"regression: boundary R0 exactly one","expected":[1.0,0.0,0.0],"passed":false},{"actual":[5.529954,0.819167,null],"check":"control: 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\": \"control: measles-like high R0\", \"actual\": [14.4, 0.930556, 0.979532], \"expected\": [14.4, 0.930556, 0.979532], \"passed\": true}, {\"check\": \"control: flu-like moderate R0 with births\", \"actual\": [1.780627, 0.4384, 0.730667], \"expected\": [1.780627, 0.4384, 0.730667], \"passed\": true}, {\"check\": \"control: exact R0 of two with half efficacy\", \"actual\": [2.0, 0.5, 1.0], \"expected\": [2.0, 0.5, 1.0], \"passed\": true}, {\"check\": \"regression: subcritical with mortality\", \"actual\": [0.8, 0.0, null], \"expected\": [0.8, 0.0, 0.0], \"passed\": false}, {\"check\": \"regression: boundary R0 exactly one\", \"actual\": [1.0, 0.0, null], \"expected\": [1.0, 0.0, 0.0], \"passed\": false}, {\"check\": \"control: 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":45.438,"exit_code":1,"observations":[{"actual":[14.4,0.930556,0.979532],"check":"control: measles-like high R0","expected":[14.4,0.930556,0.979532],"passed":true},{"actual":[1.780627,0.4384,0.730667],"check":"control: flu-like moderate R0 with births","expected":[1.780627,0.4384,0.730667],"passed":true},{"actual":[2.0,0.5,1.0],"check":"control: exact R0 of two with half efficacy","expected":[2.0,0.5,1.0],"passed":true},{"actual":[0.8,-0.25,-0.277778],"check":"regression: subcritical with mortality","expected":[0.8,0.0,0.0],"passed":false},{"actual":[1.0,0.0,0.0],"check":"regression: boundary R0 exactly one","expected":[1.0,0.0,0.0],"passed":true},{"actual":[5.529954,0.819167,null],"check":"control: 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\": \"control: measles-like high R0\", \"actual\": [14.4, 0.930556, 0.979532], \"expected\": [14.4, 0.930556, 0.979532], \"passed\": true}, {\"check\": \"control: flu-like moderate R0 with births\", \"actual\": [1.780627, 0.4384, 0.730667], \"expected\": [1.780627, 0.4384, 0.730667], \"passed\": true}, {\"check\": \"control: exact R0 of two with half efficacy\", \"actual\": [2.0, 0.5, 1.0], \"expected\": [2.0, 0.5, 1.0], \"passed\": true}, {\"check\": \"regression: subcritical with mortality\", \"actual\": [0.8, -0.25, -0.277778], \"expected\": [0.8, 0.0, 0.0], \"passed\": false}, {\"check\": \"regression: boundary R0 exactly 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0.0, 0.0], \"expected\": [0.8, 0.0, 0.0], \"passed\": true}, {\"check\": \"regression: boundary R0 exactly one\", \"actual\": [1.0, 0.0, 0.0], \"expected\": [1.0, 0.0, 0.0], \"passed\": true}, {\"check\": \"control: 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\": true}\n"}},"verified":true,"visibility":"public"}