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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+language:
+  - en
+tags:
+  - substandard-falsified-medicines
+  - online-pharmacy
+  - unregistered-medicines
+  - e-commerce
+  - synthetic
+  - sub-saharan-africa
+pretty_name: Online Pharmacy & Unregistered Medicines (SSA)
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: social_media_marketplace
+    data_files: data/online_social_media.csv
+    default: true
+  - config_name: dedicated_online_pharmacy
+    data_files: data/online_dedicated.csv
+  - config_name: cross_border_ecommerce
+    data_files: data/online_cross_border.csv
+---
+
+# Online Pharmacy & Unregistered Medicines in Sub-Saharan Africa
+
+## Abstract
+
+Synthetic dataset modelling online pharmaceutical sales, unregistered medicine prevalence, and health outcomes across social media, dedicated e-pharmacies, and cross-border e-commerce in SSA. WHO estimates 50% of medicines from illegal online sites are falsified.
+
+## Parameterization Evidence
+
+| Parameter | Value | Source | Year |
+| --- | --- | --- | --- |
+| 50% of online medicines from illegal sites falsified | Prevalence | WHO | 2018 |
+| Most counterfeited: analgesics, antibiotics, ARVs, stimulants | Products | SAHPRA | 2023 |
+| Growing e-pharmacy and social media sales in Africa | Trend | Literature review | 2024 |
+
+## Validation
+
+![Validation Report](validation_report.png)
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("electricsheepafrica/online-pharmacy-unregistered", "social_media_marketplace")
+```
+
+## References
+
+1. WHO. Substandard and falsified medical products. 2018.
+2. SAHPRA. Fake medicines are a dangerous threat in Africa. 2023.
+
+## Citation
+
+```bibtex
+@dataset{electricsheepafrica_online_pharmacy_unregistered_2025,
+  title={Online Pharmacy and Unregistered Medicines in Sub-Saharan Africa},
+  author={Electric Sheep Africa},
+  year={2025},
+  publisher={HuggingFace},
+  url={https://huggingface.co/datasets/electricsheepafrica/online-pharmacy-unregistered}
+}
+```
+
+## License
+
+CC-BY-4.0

generate_dataset.py

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+"""Generate synthetic online pharmacy & unregistered medicines dataset for SSA.
+
+Research-based parameterization:
+- WHO: 50% of medicines purchased online from illegal sites are
+  falsified; growing e-pharmacy market in Africa.
+- SAHPRA: Fake medicines common; painkillers, antibiotics, antimalarials,
+  ARVs, sexual stimulants most counterfeited online.
+- SSA context: Limited regulation of online pharmacies; social media
+  sales; WhatsApp/Facebook marketplace; cross-border e-commerce.
+"""
+
+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 = {
+    "social_media_marketplace": {
+        "setting_probs": {"whatsapp": 0.30, "facebook_marketplace": 0.25,
+                          "instagram": 0.20, "telegram": 0.15, "tiktok": 0.10},
+        "product_probs": {"sexual_stimulant": 0.20, "weight_loss": 0.15,
+                          "antibiotic": 0.15, "analgesic": 0.12,
+                          "skin_lightening": 0.10, "supplement": 0.10,
+                          "antimalarial": 0.08, "other": 0.10},
+        "sf_prevalence": 0.45,
+        "unregistered_pct": 0.70,
+        "no_prescription_pct": 0.95,
+        "pharmacist_involved_pct": 0.02,
+        "verified_seller_pct": 0.05,
+    },
+    "dedicated_online_pharmacy": {
+        "setting_probs": {"dedicated_website": 0.35, "mobile_app": 0.25,
+                          "aggregator_platform": 0.20, "telemedicine_linked": 0.20},
+        "product_probs": {"antibiotic": 0.15, "antihypertensive": 0.12,
+                          "antidiabetic": 0.10, "analgesic": 0.12,
+                          "contraceptive": 0.08, "ARV": 0.05,
+                          "supplement": 0.15, "sexual_stimulant": 0.10,
+                          "other": 0.13},
+        "sf_prevalence": 0.20,
+        "unregistered_pct": 0.40,
+        "no_prescription_pct": 0.60,
+        "pharmacist_involved_pct": 0.25,
+        "verified_seller_pct": 0.30,
+    },
+    "cross_border_ecommerce": {
+        "setting_probs": {"alibaba_aliexpress": 0.30, "jumia_marketplace": 0.20,
+                          "international_website": 0.25, "dark_web": 0.10,
+                          "unknown_platform": 0.15},
+        "product_probs": {"antibiotic": 0.15, "sexual_stimulant": 0.15,
+                          "controlled_substance": 0.10, "weight_loss": 0.10,
+                          "supplement": 0.12, "antimalarial": 0.08,
+                          "skin_lightening": 0.10, "other": 0.20},
+        "sf_prevalence": 0.50,
+        "unregistered_pct": 0.80,
+        "no_prescription_pct": 0.90,
+        "pharmacist_involved_pct": 0.01,
+        "verified_seller_pct": 0.03,
+    },
+}
+
+SCENARIO_FILES = {
+    "social_media_marketplace": "online_social_media.csv",
+    "dedicated_online_pharmacy": "online_dedicated.csv",
+    "cross_border_ecommerce": "online_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))
+        platform = _choice(rng, params["setting_probs"])
+        product = _choice(rng, params["product_probs"])
+        age = int(np.clip(rng.normal(28, 10), 15, 65))
+        sex = rng.choice(["male", "female"], p=[0.50, 0.50])
+
+        no_prescription = int(rng.random() < params["no_prescription_pct"])
+        self_medication = int(no_prescription and rng.random() < 0.80)
+        pharmacist_involved = int(rng.random() < params["pharmacist_involved_pct"])
+        verified_seller = int(rng.random() < params["verified_seller_pct"])
+
+        # Registration status
+        unregistered = int(rng.random() < params["unregistered_pct"])
+        nmra_approved = int(not unregistered and rng.random() < 0.60)
+        manufacturer_known = int(rng.random() < 0.40)
+        batch_traceable = int(manufacturer_known and rng.random() < 0.30)
+
+        # Quality
+        is_sf = int(rng.random() < params["sf_prevalence"])
+        is_falsified = int(is_sf and rng.random() < 0.50)
+        is_substandard = int(is_sf and not is_falsified)
+        no_active_ingredient = int(is_falsified and rng.random() < 0.30)
+        wrong_ingredient = int(is_falsified and rng.random() < 0.15)
+        contaminated = int(is_sf and rng.random() < 0.10)
+
+        # Delivery
+        delivery_method = rng.choice(["courier", "postal", "pickup", "rider"],
+                                      p=[0.30, 0.20, 0.20, 0.30])
+        cold_chain_needed = int(product in ("vaccine", "ARV") and rng.random() < 0.30)
+        cold_chain_maintained = int(cold_chain_needed and rng.random() < 0.10)
+        delivery_days = int(np.clip(rng.exponential(5), 1, 30))
+
+        # Health outcomes
+        adr_occurred = int(is_sf and rng.random() < 0.08)
+        treatment_failure = int(is_sf and rng.random() < 0.12)
+        hospitalisation = int((adr_occurred or treatment_failure) and rng.random() < 0.10)
+        delayed_care = int(self_medication and rng.random() < 0.15)
+
+        # Regulation & enforcement
+        reported_to_nmra = int(is_sf and rng.random() < 0.02)
+        platform_removed = int(is_sf and rng.random() < 0.05)
+        consumer_aware_risk = int(rng.random() < 0.10)
+        checked_registration = int(rng.random() < 0.05)
+        price_usd = float(np.clip(rng.lognormal(np.log(3), 0.8), 0.20, 100))
+
+        any_adverse = int(adr_occurred or treatment_failure or delayed_care)
+
+        record = {
+            "record_id": f"{name[:3].upper()}-{idx:05d}",
+            "scenario": name,
+            "year": year,
+            "platform": platform,
+            "product": product,
+            "age": age,
+            "sex": sex,
+            "no_prescription": no_prescription,
+            "self_medication": self_medication,
+            "pharmacist_involved": pharmacist_involved,
+            "verified_seller": verified_seller,
+            "unregistered": unregistered,
+            "nmra_approved": nmra_approved,
+            "manufacturer_known": manufacturer_known,
+            "batch_traceable": batch_traceable,
+            "is_substandard_falsified": is_sf,
+            "is_falsified": is_falsified,
+            "no_active_ingredient": no_active_ingredient,
+            "wrong_ingredient": wrong_ingredient,
+            "contaminated": contaminated,
+            "delivery_method": delivery_method,
+            "delivery_days": delivery_days,
+            "adr_occurred": adr_occurred,
+            "treatment_failure": treatment_failure,
+            "hospitalisation": hospitalisation,
+            "delayed_care": delayed_care,
+            "reported_to_nmra": reported_to_nmra,
+            "consumer_aware_risk": consumer_aware_risk,
+            "price_usd": round(price_usd, 2),
+            "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()

requirements.txt

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+numpy>=1.24
+pandas>=2.0
+matplotlib>=3.7

validate_dataset.py

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+"""Validate synthetic online pharmacy & unregistered medicines dataset."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+SCENARIO_FILES = {
+    "social_media_marketplace": "online_social_media.csv",
+    "dedicated_online_pharmacy": "online_dedicated.csv",
+    "cross_border_ecommerce": "online_cross_border.csv",
+}
+
+COLORS = {"social_media_marketplace": "#e6550d", "dedicated_online_pharmacy": "#756bb1", "cross_border_ecommerce": "#31a354"}
+
+
+def load_data() -> pd.DataFrame:
+    frames = []
+    for scenario, filename in SCENARIO_FILES.items():
+        df = pd.read_csv(Path("data") / filename)
+        frames.append(df)
+    return pd.concat(frames, ignore_index=True)
+
+
+def plot_validation(df: pd.DataFrame, output_path: Path) -> None:
+    fig, axes = plt.subplots(4, 2, figsize=(14, 16))
+    axes = axes.flatten()
+
+    sf_cols = ["is_substandard_falsified", "is_falsified", "unregistered"]
+    sf = df.groupby("scenario")[sf_cols].mean() * 100
+    sf.plot(kind="bar", ax=axes[0])
+    axes[0].set_title("SF & Unregistered Prevalence (%)")
+    axes[0].legend(fontsize=7)
+
+    plat = df.groupby(["scenario", "platform"]).size().groupby(level=0).apply(lambda s: s / s.sum())
+    plat.unstack().plot(kind="bar", stacked=True, ax=axes[1])
+    axes[1].set_title("Platform Distribution")
+    axes[1].legend(fontsize=5)
+
+    prod = df.groupby(["scenario", "product"]).size().groupby(level=0).apply(lambda s: s / s.sum())
+    prod.unstack().plot(kind="bar", stacked=True, ax=axes[2])
+    axes[2].set_title("Product Distribution")
+    axes[2].legend(fontsize=4)
+
+    use_cols = ["no_prescription", "self_medication", "pharmacist_involved", "verified_seller"]
+    use = df.groupby("scenario")[use_cols].mean() * 100
+    use.plot(kind="bar", ax=axes[3])
+    axes[3].set_title("Purchase Patterns (%)")
+    axes[3].legend(fontsize=6)
+
+    out_cols = ["adr_occurred", "treatment_failure", "hospitalisation", "delayed_care"]
+    out = df.groupby("scenario")[out_cols].mean() * 100
+    out.plot(kind="bar", ax=axes[4])
+    axes[4].set_title("Health Outcomes (%)")
+    axes[4].legend(fontsize=7)
+
+    reg_cols = ["nmra_approved", "manufacturer_known", "batch_traceable"]
+    reg = df.groupby("scenario")[reg_cols].mean() * 100
+    reg.plot(kind="bar", ax=axes[5])
+    axes[5].set_title("Traceability & Registration (%)")
+    axes[5].legend(fontsize=7)
+
+    enf_cols = ["reported_to_nmra", "consumer_aware_risk"]
+    enf = df.groupby("scenario")[enf_cols].mean() * 100
+    enf.plot(kind="bar", ax=axes[6])
+    axes[6].set_title("Reporting & Awareness (%)")
+    axes[6].legend(fontsize=7)
+
+    for s in SCENARIO_FILES:
+        subset = df[df["scenario"] == s]
+        axes[7].hist(subset["price_usd"], bins=30, alpha=0.5, color=COLORS[s], label=s, range=(0, 30))
+    axes[7].set_title("Price Distribution (USD)")
+    axes[7].legend(fontsize=7)
+
+    plt.tight_layout()
+    fig.savefig(output_path, dpi=200)
+    plt.close(fig)
+
+
+def main() -> None:
+    df = load_data()
+    plot_validation(df, Path("validation_report.png"))
+    print("Saved validation_report.png")
+
+
+if __name__ == "__main__":
+    main()