#!/usr/bin/env python3
"""
Literature-Informed Road Traffic Injury & Trauma Dataset
=========================================================

Generates realistic synthetic records of road traffic injury patients
presenting to emergency departments in sub-Saharan Africa, including
injury mechanism, severity, prehospital care, emergency management,
and outcomes.

Target population: Road traffic injury patients of all ages presenting
to health facilities across SSA.

DAG (Sampling Order):
  1. demographics: age, sex, road_user_type
  2. injury: mechanism, body_region, severity (GCS, ISS), fractures
  3. prehospital: time_to_facility, transport, first_aid, referral
  4. emergency_care: imaging, blood, surgery, ICU
  5. complications: infection, VTE, organ_failure
  6. outcome: survived/died, disability, LOS

References (web-searched):
-----------
[1]  WHO Global Status Report on Road Safety (2023). 1.35M road
     deaths/yr. Africa highest rate: 26.6/100K vs 17.4 global.
     90% in LMICs. Pedestrians/cyclists >50% in Africa.
[2]  Galvagno SM, et al. (2019). Prehospital trauma: <10% arrive
     within golden hour in many SSA settings.
[3]  Zafar SN, et al. (Lancet 2018). Trauma care Africa: mortality
     30-40% for severe injuries (ISS>15). TBI leading cause of death.
[4]  WHO (2024). Road traffic injuries fact sheet. Young adults
     15-44y most affected. Males 3x female risk.
[5]  Chalya PL, et al. (BMC Public Health 2012). Tanzania Bugando:
     RTI mortality 11.2%. Pedestrians 41%, motorcyclists 28%.
     Head injury 44%, extremity 38%.
[6]  Hyder AA, et al. (Bull WHO 2017). Cost of RTI in LMICs:
     1-3% of GDP. Disability burden enormous.
[7]  Mwandri M, et al. (World J Emerg Surg 2020). Trauma in SSA:
     GCS on admission strongest predictor of mortality.
[8]  Mock C, et al. (Bull WHO 2012). Strengthening prehospital
     trauma care could prevent 54% of trauma deaths.
"""

import numpy as np
import pandas as pd
import argparse
import os

SCENARIOS = {
    'trauma_centre': {
        'description': 'Urban trauma centre with CT, ICU, neurosurgery, '
                       'orthopaedics, blood bank (e.g., Muhimbili, '
                       'Chris Hani Baragwanath, Kenyatta)',
        'ct_available': True,
        'icu_available': True,
        'neurosurgery_available': True,
        'blood_bank': True,
        'ambulance_rate': 0.40,
        'golden_hour_rate': 0.25,
        'mortality': 0.08,
    },
    'district_hospital': {
        'description': 'District hospital with basic X-ray, limited '
                       'surgery, no ICU (e.g., district hospitals '
                       'Tanzania, Malawi, Uganda)',
        'ct_available': False,
        'icu_available': False,
        'neurosurgery_available': False,
        'blood_bank': False,
        'ambulance_rate': 0.10,
        'golden_hour_rate': 0.10,
        'mortality': 0.18,
    },
    'rural_health_centre': {
        'description': 'Rural health centre, no imaging, no surgery, '
                       'stabilise and refer (e.g., rural Nigeria, '
                       'DRC, South Sudan)',
        'ct_available': False,
        'icu_available': False,
        'neurosurgery_available': False,
        'blood_bank': False,
        'ambulance_rate': 0.03,
        'golden_hour_rate': 0.03,
        'mortality': 0.32,
    },
}

ROAD_USER_TYPES = {
    'pedestrian': 0.38,
    'motorcyclist': 0.22,
    'motor_vehicle_occupant': 0.18,
    'cyclist': 0.10,
    'passenger_minibus': 0.08,
    'other': 0.04,
}


def generate_dataset(n=10000, seed=42, scenario='district_hospital'):
    rng = np.random.default_rng(seed)
    sc = SCENARIOS[scenario]

    users = list(ROAD_USER_TYPES.keys())
    user_p = list(ROAD_USER_TYPES.values())

    records = []

    for idx in range(n):
        rec = {'id': idx + 1}

        # ── Step 1: Demographics [4][5] ──
        r = rng.random()
        if r < 0.08:
            rec['age_years'] = rng.integers(0, 15)
        elif r < 0.40:
            rec['age_years'] = rng.integers(15, 30)
        elif r < 0.65:
            rec['age_years'] = rng.integers(30, 45)
        elif r < 0.85:
            rec['age_years'] = rng.integers(45, 60)
        else:
            rec['age_years'] = rng.integers(60, 80)

        rec['sex'] = rng.choice(['M', 'F'], p=[0.75, 0.25])

        rec['road_user_type'] = rng.choice(users, p=user_p)

        if rec['age_years'] < 15:
            rec['road_user_type'] = rng.choice(
                ['pedestrian', 'cyclist', 'passenger_minibus'],
                p=[0.60, 0.20, 0.20])

        rec['helmet_use'] = 0
        if rec['road_user_type'] == 'motorcyclist':
            rec['helmet_use'] = 1 if rng.random() < 0.25 else 0

        rec['seatbelt_use'] = 0
        if rec['road_user_type'] == 'motor_vehicle_occupant':
            rec['seatbelt_use'] = 1 if rng.random() < 0.15 else 0

        rec['alcohol_involved'] = 1 if rng.random() < 0.25 else 0

        rec['time_of_day'] = rng.choice(
            ['morning_6_12', 'afternoon_12_18', 'evening_18_24', 'night_0_6'],
            p=[0.20, 0.30, 0.30, 0.20])

        rec['road_type'] = rng.choice(
            ['highway', 'urban_road', 'rural_road', 'intersection'],
            p=[0.25, 0.30, 0.30, 0.15])

        # ── Step 2: Injury [5] ──
        rec['primary_body_region'] = rng.choice(
            ['head_neck', 'chest', 'abdomen', 'upper_extremity',
             'lower_extremity', 'spine', 'pelvis', 'multiple'],
            p=[0.28, 0.10, 0.08, 0.12, 0.20, 0.06, 0.04, 0.12])

        if rec['road_user_type'] in ('pedestrian', 'cyclist'):
            if rng.random() < 0.35:
                rec['primary_body_region'] = 'head_neck'

        rec['tbi'] = 1 if rec['primary_body_region'] == 'head_neck' else 0
        if rec['primary_body_region'] == 'multiple' and rng.random() < 0.40:
            rec['tbi'] = 1

        # GCS
        if rec['tbi']:
            gcs_roll = rng.random()
            if gcs_roll < 0.25:
                rec['gcs'] = rng.integers(3, 9)
            elif gcs_roll < 0.50:
                rec['gcs'] = rng.integers(9, 13)
            else:
                rec['gcs'] = rng.integers(13, 16)
        else:
            rec['gcs'] = rng.choice([14, 15], p=[0.20, 0.80])

        rec['gcs_category'] = 'mild'
        if rec['gcs'] <= 8:
            rec['gcs_category'] = 'severe'
        elif rec['gcs'] <= 12:
            rec['gcs_category'] = 'moderate'

        # ISS
        if rec['primary_body_region'] == 'multiple':
            rec['iss'] = max(9, int(rng.normal(25, 10)))
        elif rec['tbi'] and rec['gcs'] <= 8:
            rec['iss'] = max(16, int(rng.normal(30, 10)))
        elif rec['primary_body_region'] in ('chest', 'abdomen'):
            rec['iss'] = max(4, int(rng.normal(16, 8)))
        else:
            rec['iss'] = max(1, int(rng.normal(10, 6)))
        rec['iss'] = min(rec['iss'], 75)

        rec['iss_category'] = 'minor'
        if rec['iss'] >= 25:
            rec['iss_category'] = 'critical'
        elif rec['iss'] >= 16:
            rec['iss_category'] = 'severe'
        elif rec['iss'] >= 9:
            rec['iss_category'] = 'moderate'

        rec['fracture'] = 0
        if rec['primary_body_region'] in ('upper_extremity', 'lower_extremity', 'pelvis'):
            rec['fracture'] = 1 if rng.random() < 0.70 else 0
        elif rec['primary_body_region'] == 'multiple':
            rec['fracture'] = 1 if rng.random() < 0.50 else 0
        else:
            rec['fracture'] = 1 if rng.random() < 0.15 else 0

        rec['open_fracture'] = 0
        if rec['fracture']:
            rec['open_fracture'] = 1 if rng.random() < 0.30 else 0

        rec['internal_bleeding'] = 0
        if rec['primary_body_region'] in ('abdomen', 'chest', 'pelvis', 'multiple'):
            rec['internal_bleeding'] = 1 if rng.random() < 0.25 else 0

        rec['spinal_cord_injury'] = 0
        if rec['primary_body_region'] == 'spine':
            rec['spinal_cord_injury'] = 1 if rng.random() < 0.35 else 0

        # ── Step 3: Prehospital [2][8] ──
        tp = np.array([sc['ambulance_rate'], 0.30, 0.10, 0.20, 0.05, 0.35 - sc['ambulance_rate']])
        tp = np.maximum(tp, 0.01)
        tp = tp / tp.sum()
        rec['transport_mode'] = rng.choice(
            ['ambulance', 'private_vehicle', 'police', 'bystander',
             'walked', 'motorcycle_taxi'], p=tp)

        rec['time_to_facility_hours'] = max(0.25, round(rng.exponential(3), 1))
        rec['time_to_facility_hours'] = min(rec['time_to_facility_hours'], 48)
        rec['within_golden_hour'] = 1 if rec['time_to_facility_hours'] <= 1.0 else 0

        rec['prehospital_first_aid'] = 1 if rng.random() < 0.20 else 0
        rec['cervical_spine_immobilised'] = 0
        if rec['transport_mode'] == 'ambulance':
            rec['cervical_spine_immobilised'] = 1 if rng.random() < 0.50 else 0

        rec['referred_from_other'] = 1 if rng.random() < 0.25 else 0

        # ── Step 4: Emergency Care ──
        rec['xray_done'] = 1 if rng.random() < 0.70 else 0
        rec['ct_done'] = 0
        if sc['ct_available'] and (rec['tbi'] or rec['iss'] >= 16):
            rec['ct_done'] = 1 if rng.random() < 0.65 else 0

        rec['ultrasound_fast'] = 0
        if rec['internal_bleeding'] or rec['primary_body_region'] in ('abdomen', 'pelvis'):
            rec['ultrasound_fast'] = 1 if rng.random() < 0.40 else 0

        rec['blood_transfusion'] = 0
        if rec['internal_bleeding'] or rec['iss'] >= 25:
            if sc['blood_bank']:
                rec['blood_transfusion'] = 1 if rng.random() < 0.45 else 0
            else:
                rec['blood_transfusion'] = 1 if rng.random() < 0.10 else 0

        rec['surgery_performed'] = 0
        rec['surgery_type'] = 'none'
        if rec['internal_bleeding'] and rng.random() < 0.50:
            rec['surgery_performed'] = 1
            rec['surgery_type'] = 'laparotomy'
        elif rec['tbi'] and rec['gcs'] <= 8 and sc['neurosurgery_available']:
            rec['surgery_performed'] = 1 if rng.random() < 0.30 else 0
            if rec['surgery_performed']:
                rec['surgery_type'] = 'craniotomy'
        elif rec['fracture']:
            rec['surgery_performed'] = 1 if rng.random() < 0.35 else 0
            if rec['surgery_performed']:
                rec['surgery_type'] = rng.choice(
                    ['orif', 'external_fixation', 'amputation'],
                    p=[0.50, 0.35, 0.15])

        rec['icu_admission'] = 0
        if sc['icu_available'] and (rec['gcs'] <= 8 or rec['iss'] >= 25):
            rec['icu_admission'] = 1 if rng.random() < 0.40 else 0

        rec['intubated'] = 0
        if rec['gcs'] <= 8:
            rec['intubated'] = 1 if rng.random() < (0.60 if sc['icu_available'] else 0.10) else 0

        rec['tetanus_given'] = 1 if rng.random() < 0.65 else 0
        rec['antibiotics_given'] = 1 if (rec['open_fracture'] or rec['surgery_performed']) else 0

        # ── Step 5: Complications ──
        rec['wound_infection'] = 0
        if rec['open_fracture'] or rec['surgery_performed']:
            rec['wound_infection'] = 1 if rng.random() < 0.15 else 0

        rec['sepsis'] = 0
        if rec['wound_infection'] or (rec['icu_admission'] and rng.random() < 0.10):
            rec['sepsis'] = 1 if rng.random() < 0.25 else 0

        rec['vte'] = 0
        if rec['fracture'] and rec['primary_body_region'] in ('lower_extremity', 'pelvis'):
            rec['vte'] = 1 if rng.random() < 0.05 else 0

        # ── Step 6: Outcome [3][7] ──
        mort = sc['mortality']
        if rec['gcs'] <= 8:
            mort *= 4.0
        elif rec['gcs'] <= 12:
            mort *= 2.0
        if rec['iss'] >= 25:
            mort *= 2.5
        if rec['internal_bleeding'] and not rec['blood_transfusion']:
            mort *= 2.0
        if not rec['within_golden_hour'] and rec['iss'] >= 16:
            mort *= 1.5
        if rec['age_years'] > 60:
            mort *= 1.5
        if rec['age_years'] < 5:
            mort *= 1.3
        if rec['surgery_performed'] and rec['internal_bleeding']:
            mort *= 0.50
        if rec['icu_admission']:
            mort *= 0.70

        rec['outcome'] = 'died' if rng.random() < min(mort, 0.80) else 'survived'

        if rec['outcome'] == 'died':
            rec['length_of_stay_days'] = max(0, rng.integers(0, 7))
        elif rec['iss'] >= 16:
            rec['length_of_stay_days'] = max(3, int(rng.normal(18, 10)))
        else:
            rec['length_of_stay_days'] = max(1, int(rng.normal(5, 3)))

        rec['disability_at_discharge'] = 'none'
        if rec['outcome'] == 'survived':
            if rec['spinal_cord_injury']:
                rec['disability_at_discharge'] = 'paralysis'
            elif rec['surgery_type'] == 'amputation':
                rec['disability_at_discharge'] = 'amputation'
            elif rec['tbi'] and rec['gcs'] <= 12:
                rec['disability_at_discharge'] = 'cognitive_impairment'
            elif rec['fracture']:
                rec['disability_at_discharge'] = 'mobility_limitation'

        records.append(rec)

    df = pd.DataFrame(records)

    print(f"\n{'='*65}")
    print(f"Road Traffic Injury — {scenario} (n={n}, seed={seed})")
    print(f"{'='*65}")

    print(f"\n  Male: {(df['sex']=='M').mean()*100:.1f}%")
    print(f"  Pedestrians: {(df['road_user_type']=='pedestrian').mean()*100:.1f}%")
    print(f"  TBI: {df['tbi'].mean()*100:.1f}%")
    print(f"  Severe (ISS≥16): {(df['iss']>=16).mean()*100:.1f}%")
    print(f"  Within golden hour: {df['within_golden_hour'].mean()*100:.1f}%")
    print(f"  Ambulance: {(df['transport_mode']=='ambulance').mean()*100:.1f}%")

    died = (df['outcome'] == 'died').sum()
    print(f"  Mortality: {died} ({died/n*100:.1f}%)")

    return df


if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description='Generate synthetic road traffic injury dataset')
    parser.add_argument('--scenario', type=str, default='district_hospital',
                        choices=list(SCENARIOS.keys()))
    parser.add_argument('--n', type=int, default=10000)
    parser.add_argument('--seed', type=int, default=42)
    parser.add_argument('--output', type=str, default=None)
    parser.add_argument('--all-scenarios', action='store_true')
    args = parser.parse_args()

    os.makedirs('data', exist_ok=True)

    if args.all_scenarios:
        for sc_name in SCENARIOS:
            df = generate_dataset(n=args.n, seed=args.seed, scenario=sc_name)
            out = os.path.join('data', f'rti_{sc_name}.csv')
            df.to_csv(out, index=False)
            print(f"  → Saved to {out}\n")
    else:
        df = generate_dataset(n=args.n, seed=args.seed, scenario=args.scenario)
        out = args.output or os.path.join('data', f'rti_{args.scenario}.csv')
        df.to_csv(out, index=False)
        print(f"  → Saved to {out}")