"""Generate synthetic antibiotic quality & AMR acceleration dataset for SSA. Research-based parameterization: - BMJ Global Health (2022): SF antibiotics are neglected drivers of AMR; sulfamethoxazole-trimethoprim highest failure frequency, followed by ampicillin, amoxicillin, ciprofloxacin, tetracycline. - BMJ Global Health (2025): SF antibiotics quantitatively associated with AMR prevalence. - PLOS ONE (2024): SF antimicrobials in East Africa; dynamic and evolving problem. - Frontiers (2025): SF antibiotics particularly prevalent in Africa; contributes to AMR burden. - CIDRAP: API issues most frequent quality problem; highest failure rates in Africa and Asia. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd SEED = 42 N_PER_SCENARIO = 10_000 YEAR_RANGE = np.arange(2010, 2025) YEAR_WEIGHTS = np.linspace(0.85, 1.3, len(YEAR_RANGE)) YEAR_WEIGHTS = YEAR_WEIGHTS / YEAR_WEIGHTS.sum() SCENARIOS = { "community_otc_access": { "setting_probs": {"community_pharmacy": 0.30, "drug_shop": 0.30, "informal_vendor": 0.25, "market_stall": 0.15}, "antibiotic_probs": {"amoxicillin": 0.25, "cotrimoxazole": 0.20, "ciprofloxacin": 0.15, "metronidazole": 0.12, "doxycycline": 0.10, "ampicillin": 0.08, "tetracycline": 0.05, "azithromycin": 0.05}, "sf_prevalence": 0.30, "no_prescription_pct": 0.70, "incomplete_course_pct": 0.45, "api_failure_pct": 0.25, "amr_base_rate": 0.35, "quality_tested_pct": 0.02, }, "hospital_referral": { "setting_probs": {"district_hospital": 0.35, "regional_hospital": 0.25, "tertiary_hospital": 0.20, "private_clinic": 0.20}, "antibiotic_probs": {"amoxicillin": 0.15, "ceftriaxone": 0.20, "ciprofloxacin": 0.15, "gentamicin": 0.10, "metronidazole": 0.10, "azithromycin": 0.10, "meropenem": 0.05, "vancomycin": 0.05, "cotrimoxazole": 0.10}, "sf_prevalence": 0.12, "no_prescription_pct": 0.15, "incomplete_course_pct": 0.20, "api_failure_pct": 0.10, "amr_base_rate": 0.40, "quality_tested_pct": 0.08, }, "cross_border_unregulated": { "setting_probs": {"border_market": 0.35, "unlicensed_shop": 0.25, "mobile_vendor": 0.20, "online_seller": 0.20}, "antibiotic_probs": {"amoxicillin": 0.20, "cotrimoxazole": 0.20, "tetracycline": 0.15, "ciprofloxacin": 0.12, "ampicillin": 0.10, "chloramphenicol": 0.08, "doxycycline": 0.08, "erythromycin": 0.07}, "sf_prevalence": 0.42, "no_prescription_pct": 0.85, "incomplete_course_pct": 0.55, "api_failure_pct": 0.35, "amr_base_rate": 0.45, "quality_tested_pct": 0.01, }, } SCENARIO_FILES = { "community_otc_access": "antibiotic_community.csv", "hospital_referral": "antibiotic_hospital.csv", "cross_border_unregulated": "antibiotic_cross_border.csv", } def _choice(rng, prob_map): keys = list(prob_map.keys()) weights = np.array(list(prob_map.values()), dtype=float) weights = weights / weights.sum() return rng.choice(keys, p=weights) def _simulate_scenario(name, params, seed): rng = np.random.default_rng(seed) records = [] for idx in range(N_PER_SCENARIO): year = int(rng.choice(YEAR_RANGE, p=YEAR_WEIGHTS)) setting = _choice(rng, params["setting_probs"]) age = int(np.clip(rng.normal(25, 18), 0, 80)) sex = rng.choice(["male", "female"], p=[0.48, 0.52]) antibiotic = _choice(rng, params["antibiotic_probs"]) indication = rng.choice(["respiratory_infection", "UTI", "skin_wound", "diarrhoea", "STI", "surgical_prophylaxis", "other"], p=[0.25, 0.15, 0.15, 0.15, 0.10, 0.10, 0.10]) no_prescription = int(rng.random() < params["no_prescription_pct"]) self_medication = int(no_prescription and rng.random() < 0.70) incomplete_course = int(rng.random() < params["incomplete_course_pct"]) dose_subtherapeutic = int(rng.random() < 0.15) manufacturer = rng.choice(["local_generic", "indian_generic", "chinese_generic", "who_prequalified", "branded_originator", "unknown"], p=[0.15, 0.30, 0.20, 0.15, 0.10, 0.10]) # Quality is_sf = int(rng.random() < params["sf_prevalence"]) is_falsified = int(is_sf and rng.random() < 0.30) is_substandard = int(is_sf and not is_falsified) if is_falsified: api_pct = float(np.clip(rng.normal(10, 15), 0, 40)) elif is_substandard: api_pct = float(np.clip(rng.normal(60, 15), 20, 84)) else: api_pct = float(np.clip(rng.normal(97, 4), 85, 115)) api_failure = int(api_pct < 85) dissolution_failure = int(is_sf and rng.random() < 0.40) contamination_detected = int(is_sf and rng.random() < 0.05) wrong_ingredient = int(is_falsified and rng.random() < 0.15) # AMR outcomes sf_amr_mult = 1.8 if is_sf else 1.0 incomplete_mult = 1.5 if incomplete_course else 1.0 amr_detected = int(rng.random() < np.clip( params["amr_base_rate"] * sf_amr_mult * incomplete_mult, 0, 0.80)) esbl_producer = int(amr_detected and rng.random() < 0.25) mrsa = int(amr_detected and rng.random() < 0.10) mdr = int(amr_detected and rng.random() < 0.15) # Clinical outcomes treatment_failure = int(rng.random() < np.clip( 0.10 * sf_amr_mult * incomplete_mult, 0, 0.40)) hospitalisation = int(treatment_failure and rng.random() < 0.20) sepsis = int(hospitalisation and rng.random() < 0.15) death = int(sepsis and rng.random() < 0.10) adr = int(contamination_detected or (is_sf and rng.random() < 0.03)) # Surveillance quality_tested = int(rng.random() < params["quality_tested_pct"]) culture_sensitivity_done = int(setting in ("district_hospital", "regional_hospital", "tertiary_hospital") and rng.random() < 0.15) amr_reported = int(culture_sensitivity_done and amr_detected and rng.random() < 0.50) antibiogram_available = int(culture_sensitivity_done and rng.random() < 0.30) any_adverse = int(treatment_failure or adr or death) record = { "record_id": f"{name[:3].upper()}-{idx:05d}", "scenario": name, "year": year, "setting": setting, "age": age, "sex": sex, "antibiotic": antibiotic, "indication": indication, "no_prescription": no_prescription, "self_medication": self_medication, "incomplete_course": incomplete_course, "manufacturer": manufacturer, "is_substandard_falsified": is_sf, "is_falsified": is_falsified, "is_substandard": is_substandard, "api_pct_label": round(api_pct, 1), "api_failure": api_failure, "dissolution_failure": dissolution_failure, "wrong_ingredient": wrong_ingredient, "amr_detected": amr_detected, "esbl_producer": esbl_producer, "mrsa": mrsa, "mdr": mdr, "treatment_failure": treatment_failure, "hospitalisation": hospitalisation, "sepsis": sepsis, "death": death, "adr": adr, "quality_tested": quality_tested, "culture_sensitivity_done": culture_sensitivity_done, "amr_reported": amr_reported, "any_adverse": any_adverse, } records.append(record) return pd.DataFrame(records) def main(): output_dir = Path("data") output_dir.mkdir(parents=True, exist_ok=True) for idx, (name, params) in enumerate(SCENARIOS.items()): df = _simulate_scenario(name, params, SEED + idx * 211) df.to_csv(output_dir / SCENARIO_FILES[name], index=False) print(f"Saved {name} -> {SCENARIO_FILES[name]}") if __name__ == "__main__": main()