{"self":"http://probly.dev/api/sim/ER5SDZNdJmMQY3kssahZVY/?format=json","id":"ER5SDZNdJmMQY3kssahZVY","created_at":"2026-04-08T23:57:03.627413Z","status":{"status":"SUCCESS","status_datetime":"2026-05-01T08:54:44.976098Z","time_elapsed":null,"done_at":null},"input_type":"esty","spreadsheet_input":null,"esty_input":{"content":"_cohort = 10 * 1000  # Arbitrary\n\n# Iron-deficiency anemia prevalence, children in India (any severity)\n# Global Burden of Disease 2016, see 'Supplementary data'\np_anemia = Beta(19, 22)  # p15=0.38, p85=0.54; beta by quantiles not yet supported\n\n\n\ndef anemia_reduction():\n    \"\"\"\n    In YLDs per cohort\n    \"\"\"\n    # Of children with anemia, proportion with mild, moderate, and severe anemia\n    p_severity = {\n        \"mild\": 48 / 100,\n        \"moderate\": 48 / 100,\n        \"severe\": 4.1 / 100\n    }\n\n    # Risk ratio anemia, iron supplementation\n    risk_ratio = PERT(0.3, 0.5, 1)\n\n    p_cured = {}\n    for severity in p_severity:\n        p_cured[severity] = p_anemia * p_severity[severity] * risk_ratio\n\n    yld = {\n        \"mild\": 0.4 / 100,\n        \"moderate\": 5.2 / 100,\n        \"severe\": 15 / 100\n    }\n\n    yld_sum = 0\n    for severity in yld:\n        yld_sum += p_cured[severity] * yld[severity]\n\n    return _cohort * yld_sum\n\ndef cognitive_benefits():\n    \"\"\"\n    In (present value of) units of increase in ln(income) per cohort.\n\n    These drive most of the cost-effectiveness.\n    \"\"\"\n    # The intervention targets all children in a school, but we model\n    # cognitive benefits as accruing only to children between ages 3-12.\n    # Proportion of targeted children between ages 3-12\n    p_children = 65 / 100\n\n    # Change in IQ points from short-term supplementation for anemic individuals\n    iq_change_sd = LogNormal(0.33 td 2)\n    iq_change = iq_change_sd * 15  # Convert to IQ points: 1 SD = 15 IQ points\n\n    p_wages_per_point = 1 / 100  # Percentage increase in wages/consumption for every 1 point increase in IQ\n\n    # Percentage increase in wages/consumption from increase in IQ from supplementation\n    p_wages = iq_change * p_wages_per_point\n\n    # Speculative adjustment to assess the long-term benefit of many years of iron\n    long_term_adjustment = 10 / 100\n    p_wages = p_wages * long_term_adjustment\n\n    # Increase in annual ln(consumption) for beneficiaries\n    change_ln_cons = math.log(1 + p_wages) - math.log(1)\n\n    delay_y = 12  # Average number of years between 'entering fortification program' and beginning of long term benefits\n    duration_y = LogUniform(15 td 3)  # Duration of long term benefits of fortification (in years)\n\n    # Present Value Income Benefits\n    discount_rate = Uniform(2/100, 7/100)\n    pv_income = pv(discount_rate, delay_y + duration_y, -change_ln_cons) - pv(\n        discount_rate, delay_y, -change_ln_cons\n    )  # Unit is still changes in ln(income)\n\n    # Multiplier for Resource Sharing in Households\n    multiplier_resource_sharing = 2\n    pv_income = pv_income * multiplier_resource_sharing\n\n    # Lifetime Income Benefits for Full Cohort (including non-beneficiaries)\n    return _cohort * p_children * p_anemia * pv_income\n\n\ndef altered_malaria_risk():\n    \"\"\"\n    In deaths per cohort\n    \"\"\"\n    baseline_deaths_100k = 5.14  # Baseline deaths due to malaria per 100,000 individuals 0-19 in India\n    # Convert to per cohort\n    baseline_deaths = baseline_deaths_100k * (_cohort / (100 * 1000))\n\n    rr = PERT(1, 1.16, 1.5)  # Relative risk of malaria mortality with IFA supplementation\n\n    direct = baseline_deaths * (rr - 1)\n    indirect_per_direct = 50 / 100\n    return direct * (1 + indirect_per_direct)\n\n\ndef other_adverse_effects():\n    \"\"\"\n    In YLDs per cohort\n    \"\"\"\n\n    p_condition = {\n        \"gastro\": (8 + 1 + 33 + 2 + 8) / (23 + 12 + 128 + 2 + 8),\n        \"loose_stools\": (1 + 2 + 6 + 2 + 1 + 3) / (36 + 8 + 128 + 6 + 44 + 71),\n        \"hard_stools\": (1 + 5 + 0 + 0 + 11 + 0 + 6 + 3) / (31 + 36 + 8 + 182 + 128 + 6 + 44 + 71),\n        \"abdominal_pain\": (0 + 4 + 2 + 15 + 1 + 3 + 4) / (120 + 36 + 182 + 128 + 6 + 44 + 71),\n    }\n\n    yld = {\n        \"gastro\": 7.4 / 100,\n        \"loose_stools\": 7.4 / 100,\n        \"hard_stools\": 7.4 / 100,\n        \"abdominal_pain\": 1.1 / 100,\n    }\n\n    duration = 2 / 365\n\n    yld_sum = 0\n    for condition in yld:\n        yld_sum += p_condition[condition] * yld[condition] * duration\n\n    return _cohort * yld_sum\n\n\ndef yld_to_val(yld):\n    \"\"\"\n    YLDs to units of value. Arbitrary normalization.\n    \"\"\"\n    return 1 * yld\n\n\ndef ln_cons_to_val(ln_cons):\n    \"\"\"\n    Units of increase in ln(consumption) to units of value\n    \"\"\"\n    value_double = Uniform(0.2, 0.8)  # Rule of thumb that 1 DALY = 2.5x GDP per capita would give 0.4\n    value_1_ln = value_double / math.log(2)\n    return value_1_ln * ln_cons\n\n\ndef death_to_val(death):\n    \"\"\"\n    Deaths to units of value\n    \"\"\"\n    return 30 * death\n\n\ndef units_value():\n    \"\"\"\n    In \"units of value\" per cohort (normalized to 1 unit of value = 1 YLD).\n    \"\"\"\n\n    anemia_ylds_averted = anemia_reduction()\n    units_increase_ln_income = cognitive_benefits()\n    malaria_deaths = altered_malaria_risk()\n    side_effect_ylds = other_adverse_effects()\n\n    u_value = {\n        \"anemia\": yld_to_val(anemia_ylds_averted),\n        \"income\": ln_cons_to_val(units_increase_ln_income),\n        \"malaria\": -death_to_val(malaria_deaths),\n        \"side_effect\": -yld_to_val(side_effect_ylds),\n    }\n\n    return sum(u_value.values())\n\ndef cost_effectiveness():\n    \"\"\"\n    Units of value per $10,000\n    \"\"\"\n    # Supplementation cost per person treated\n    cost_p_p = 2\n    cost_cohort = _cohort * cost_p_p\n    val_cohort = units_value()\n    return val_cohort * (10 * 1000 / cost_cohort)\n\n\ndef ratio_givedirectly():\n    \"\"\"\n    Cost-effectiveness ratio vs GiveDirectly\n    \"\"\"\n    # GiveDirectly units of increase in ln(consumption) for each $10,000 donated\n    gd_ln_cons = 25\n    return cost_effectiveness() / ln_cons_to_val(gd_ln_cons)\n\n\ndef pv(rate, nper, pmt):\n    temp = math.pow((1 + rate), nper)\n    fact = (temp - 1) / rate\n    return -(pmt * fact) / temp\n\n\nvalue_per_10_000_usd = cost_effectiveness()\nmultiples_of_cash = 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