--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression - time-series-forecasting language: - en tags: - synthetic - healthcare - epidemiology - pandemic - seird - agent-based - contact-network - sars-cov-2 - influenza - measles - ebola - mers - mpox - variant-emergence - polymod - reproduction-number - rt-estimation - ifr - attack-rate - cdc - who - levin-2022 - mossong-2008 - cori-2013 - vaccination - waning-immunity - npi - transmission-dynamics pretty_name: HLT-012 Synthetic Pandemic Spread Dataset — SEIRD + Contact Network (Sample Preview) size_categories: - 1K ⚠️ **PRIVACY & SYNTHETIC NATURE** > Every record in this dataset is **100% synthetic**. **No real patient data, no PHI, no real epidemiological surveillance records.** Compartment dynamics, attack rates, and IFR follow published WHO / CDC / Levin 2022 / POLYMOD references but the simulation is computationally generated. --- ## What's in this sample — 3 distinct epidemic dynamics This preview demonstrates the engine's range by running **3 pathogen scenarios** with very different transmission dynamics: | Scenario | Pathogen | R0 | Vax cov | Days | Pop | Result | |---|---|---|---|---|---|---| | **Mature epidemic** | SARS-CoV-2 | 2.5 | 30% | 180 | 15,000 | Peak I~1700 (day ~145), attack 84%, 72 deaths, late Rt 0.42 | | **Marginal transmission** | Influenza-A | 1.4 | 40% | 120 | 15,000 | Peak I~8 (day ~34), attack 37%, late Rt ~1.10 (limping along) | | **Burn-through outbreak** | Measles | 15.0 | 85% | 240 | 15,000 | Peak I~1700 (day ~76), attack 98%, late Rt 0.16 (extinguished) | These three shapes — **slow buildup → mature peak / low-R0 marginal / explosive burn-through** — span the full operating range of pandemic modeling: COVID-era surveillance scenarios, seasonal flu monitoring, and outbreak investigation of high-R0 vaccine-preventable diseases. ### Files | File | Rows × Cols | Description | |---|---|---| | `sars_cov2_epidemic_timeseries.csv` | 180 × 26 | Daily SEIRD compartments + Rt + variants + hospitalizations | | `sars_cov2_contact_network.csv` | ~7,800 × 12 | Transmission events: infector → infectee, setting, age, variant | | `influenza_a_epidemic_timeseries.csv` | 120 × 26 | Slow-burning flu season timeseries | | `influenza_a_contact_network.csv` | ~25 × 12 | Sparse transmission events (low R0) | | `measles_epidemic_timeseries.csv` | 240 × 26 | Burn-through outbreak timeseries | | `measles_contact_network.csv` | ~6,400 × 12 | Rapid transmission cascade events | | `pathogen_profiles.json` | 12 pathogens | Full catalog of all 12 pathogen profiles available in the product | **Total:** ~1.1 MB across 8 files. --- ## Schema highlights ### `*_epidemic_timeseries.csv` (26 columns per scenario) **Compartments (SEIRD):** `day`, `date_offset`, `S` (Susceptible), `E` (Exposed), `I` (Infectious), `R` (Recovered), `D` (Deceased), `N` (population) **Incidence:** `new_exposed`, `new_infectious`, `new_hospitalizations`, `icu_occupancy` **Transmission tracking:** `Rt_estimated` (renewal equation per Cori et al. 2013), `attack_rate_cumulative`, `effective_R0` **Variant dynamics:** `dominant_variant` (Wildtype / Alpha-proxy / Delta-proxy / Omicron-proxy), `variant_R0_mult`, `variant_immune_escape`, `variant_severity_mult` **Population immunity:** `n_vaccinated`, `n_boosted`, `mean_immunity_level` **Metadata:** `pathogen`, `seed_county_fips` (LA County), `simulation_id` ### `*_contact_network.csv` (12 columns per scenario) `day`, `agent_id`, `infector_id`, `contact_type` (household / workplace / school / community / transit), `contact_duration_min`, `contact_weight`, `age_band_agent`, `age_band_infector`, `household_id_agent`, `household_id_infector`, `variant`, `immune_escape` ### `pathogen_profiles.json` — 12 pathogens | Pathogen | R0 | Serial interval (days) | IFR (75+) | Symptomatic fraction | |---|---|---|---|---| | SARS-CoV-2 | 2.5 | 5.1 | 5.4% | 60% | | Influenza-A | 1.4 | 3.0 | 1.2% | 70% | | Measles | 15.0 | 11.0 | 0.6% | 95% | | Ebola | Variable | — | — | — | | MERS | Variable | — | — | — | | Mpox | Variable | — | — | — | | Plague | Variable | — | — | — | | Cholera | Variable | — | — | — | | RSV | Variable | — | — | — | | Influenza-B | Variable | — | — | — | | Smallpox | Variable | — | — | — | | Novel-Pathogen | Variable | — | — | — | The 3 fully-calibrated pathogens (SARS-CoV-2, Influenza-A, Measles) have age-stratified IFR and hospitalization rates from Levin 2022 / CDC FluView / WHO sources. The remaining 9 use parameterized variants of SARS-CoV-2 with randomized R0 — useful for sensitivity testing and novel-pathogen scenario planning. --- ## Calibration source story The full HLT-012 generator anchors all distributions to authoritative epidemiological references: - **CDC COVID-19 Surveillance Data (2020-2023)** — SARS-CoV-2 attack rates, serial interval Gamma(5.1, 2.6), pre-vaccination peak Rt 2-4 - **Levin et al. (2022) Eur J Epidemiol** — Age-stratified IFR meta-analysis - **Mossong et al. (2008) PLoS Med** — POLYMOD contact survey age-stratified contact matrix; mean 12-20 contacts/day - **CDC FluView** — Influenza-A seasonal attack rates 5-20%, R0 1.2-1.6, IFR 0.001-0.012 - **WHO Measles Fact Sheet** — R0 12-18, attack rate >90% in non-vaccinated cohorts, CFR 0.2-2% - **CDC Immunization Information System** — Vaccination coverage by age band - **Hodcroft et al. (2021)** — SARS-CoV-2 variant emergence sequence with R0 multipliers - **US Census 2020** — Age distribution by band (0-4: 6%, 5-17: 16%, ...) - **Cori et al. (2013) Am J Epidemiol** — Rt renewal equation methodology ### Sample-scale validation scorecard | Metric | Observed | Target | Status | Source | |---|---|---|---|---| | Scenario count | 3 | 3 | ✅ PASS | Schema invariant | | SARS-CoV-2 attack rate | 84.4% | 85% ± 10% | ✅ PASS | CDC COVID Surveillance | | Influenza attack rate | 37.0% | 37% ± 15% | ✅ PASS | CDC FluView + initial immunity | | Measles attack rate | 97.9% | 97% ± 5% | ✅ PASS | WHO Measles Fact Sheet | | SARS-CoV-2 late Rt | 0.42 | ≤ 1.0 | ✅ PASS | Cori et al. (2013) | | Measles late Rt | 0.16 | ≤ 1.0 | ✅ PASS | WHO measles dynamics | | Compartment conservation | 100% | 100% | ✅ PASS | SEIRD mass invariant | | Mean contact degree | 10.7 | 14 ± 6 | ✅ PASS | Mossong (2008) POLYMOD | | Age band diversity count | 6 | 6 | ✅ PASS | US Census 2020 partition | | Pathogen profile count | 12 | 12 | ✅ PASS | Product catalog | **Grade: A+ (100/100) — verified across 6 random seeds (42, 7, 123, 2024, 99, 1).** --- ## Loading examples ### Pandas — epidemic curve plot ```python import pandas as pd import matplotlib.pyplot as plt sars = pd.read_csv("sars_cov2_epidemic_timeseries.csv") flu = pd.read_csv("influenza_a_epidemic_timeseries.csv") meas = pd.read_csv("measles_epidemic_timeseries.csv") fig, axes = plt.subplots(3, 1, figsize=(10, 9), sharex=False) for ax, df, title in zip(axes, [sars, flu, meas], ["SARS-CoV-2 (R0=2.5, 30% vax)", "Influenza-A (R0=1.4, 40% vax)", "Measles (R0=15, 85% vax)"]): ax.plot(df["day"], df["S"], label="S", color="#4477aa") ax.plot(df["day"], df["E"], label="E", color="#ee6677") ax.plot(df["day"], df["I"], label="I", color="#cc3311") ax.plot(df["day"], df["R"], label="R", color="#228833") ax.plot(df["day"], df["D"] * 50, label="D × 50", color="#000000", linestyle="--") ax.set_title(title) ax.legend(loc="center right") plt.tight_layout() plt.show() ``` ### Rt trajectory analysis ```python import pandas as pd sars = pd.read_csv("sars_cov2_epidemic_timeseries.csv") # Rt below 1 detection (epidemic ending) rt = sars["Rt_estimated"] below_1_first_day = rt[rt < 1].index[0] if (rt < 1).any() else None print(f"Rt first crossed below 1 on day: {below_1_first_day}") # Variant emergence print("\nVariant dominance by day:") print(sars.groupby("dominant_variant")["day"].agg(["min", "max"])) ``` ### Contact network analysis ```python import pandas as pd net = pd.read_csv("sars_cov2_contact_network.csv") # Transmission by setting print("Transmissions by contact setting:") print(net["contact_type"].value_counts(normalize=True).round(3)) # Age-band transmission matrix (who infects whom) print("\nAge-band transmission matrix:") matrix = pd.crosstab(net["age_band_infector"], net["age_band_agent"], normalize="all").round(3) print(matrix) # Household secondary attack rate (transmissions within household) hh_transmissions = (net["household_id_agent"] == net["household_id_infector"]).sum() print(f"\nHousehold transmissions: {hh_transmissions} / {len(net)} = " f"{hh_transmissions/len(net):.1%}") ``` ### Pathogen profile inspection ```python import json with open("pathogen_profiles.json") as f: profiles = json.load(f) for name, prof in profiles.items(): print(f"{name:20} R0={prof['R0_base']:.2f} " f"serial={prof['serial_interval_mean']:.1f}d " f"IFR(75+)={prof['IFR_by_age']['75+']*100:.2f}%") ``` ### Rt forecasting baseline (ML) ```python import pandas as pd from sklearn.ensemble import GradientBoostingRegressor sars = pd.read_csv("sars_cov2_epidemic_timeseries.csv") # Build features: 7-day lagged compartment fractions df = sars.copy() for lag in [1, 3, 7, 14]: df[f"I_lag{lag}"] = df["I"].shift(lag) df[f"S_frac_lag{lag}"] = (df["S"] / df["N"]).shift(lag) df[f"Rt_lag{lag}"] = df["Rt_estimated"].shift(lag) df = df.dropna() feat_cols = [c for c in df.columns if c.startswith(("I_lag", "S_frac_lag", "Rt_lag"))] target = df["Rt_estimated"] m = GradientBoostingRegressor(random_state=42).fit(df[feat_cols], target) print(f"Rt forecasting R² (in-sample): {m.score(df[feat_cols], target):.3f}") ``` ### Hugging Face Datasets ```python from datasets import load_dataset ds = load_dataset("xpertsystems/hlt012-sample", data_files={ "sars_cov2": "sars_cov2_epidemic_timeseries.csv", "sars_cov2_net": "sars_cov2_contact_network.csv", "influenza": "influenza_a_epidemic_timeseries.csv", "measles": "measles_epidemic_timeseries.csv", }) print(ds) ``` --- ## Suggested use cases - **Rt forecasting / nowcasting** — train models to predict next-week Rt from rolling compartment features and recent transmission events - **Variant emergence detection** — classify epidemic regime shifts (Wildtype → Alpha → Delta → Omicron) from compartment dynamics - **Outbreak shape classification** — distinguish slow-burn from burn-through dynamics using early compartment features - **Synthetic surveillance pipeline testing** — validate epidemiological ETL/dashboard systems with schema-compliant synthetic data - **Pandemic preparedness scenarios** — counterfactual analysis (what if R0=3.5 instead of 2.5? what if vax coverage was 70%?) - **Contact network graph ML** — train GNNs on infector→infectee edges with age/setting/variant features - **Healthcare AI pretraining** — pretrain epidemic forecasting models before fine-tuning on real surveillance data - **NPI policy modeling** — analyze how NPI dampening interacts with R0 and immunity buildup - **Vaccination strategy analysis** — compare attack rates and deaths across vaccination coverage levels - **Hospital surge planning** — use `new_hospitalizations` and `icu_occupancy` trajectories for capacity modeling - **Educational use** — undergraduate epidemiology, biostatistics, and computational health courses --- ## Sample vs. full product | Aspect | This sample | Full HLT-012 product | |---|---|---| | Population per simulation | 15,000 | 100,000+ (default) up to 10M | | Pathogens in preview | 3 (SARS-CoV-2 / Influenza-A / Measles) | All 12 fully configurable | | Scenarios | 3 pre-built | Unlimited (any pathogen × vax × duration × seed) | | Schema | identical | identical | | Calibration | identical | identical | | License | CC-BY-NC-4.0 | Commercial license | The full product unlocks: - **Up to 10M agent populations** for metropolitan-scale outbreak modeling - **All 12 pathogen profiles** including outbreak investigation scenarios (Ebola, MERS, Mpox, Plague) - **Multi-region geographic spread** (county-level FIPS routing) - **Custom intervention layering** — NPIs, mask mandates, vaccination campaigns - **Multi-strain co-circulation** dynamics - Commercial use rights **Contact us for the full product.** --- ## Limitations & honest disclosures - **Sample is preview-only.** 3 scenarios × 15K agents is enough to demonstrate schema, calibration, and dynamics range, but is **not statistically sufficient** for production-grade Rt forecasting or outbreak detection model training. Use the full product for serious work. - **Generator patch required** (v1.0.1+). The v1.0.0 generator has a known crash at line 349 when the infectious compartment empties mid-simulation (`int(NaN)` on `.mean()` of empty array). The v1.0.1 patch adds a defensive guard mirroring the existing line 345 pattern. This sample was generated with v1.0.1. The fix is a 1-line change documented in the generator's `CHANGELOG`. - **Mean contact degree at sample scale (~11/day) runs slightly below POLYMOD target (12-20).** This is a generator artifact — at 15K population the workplace and school assignments are sparser than at 100K+, reducing mean network density. The full product hits the POLYMOD range at scale. - **3 of 12 pathogens are fully calibrated; 9 use SARS-CoV-2-templated defaults with randomized R0.** Ebola, MERS, Mpox, Plague, Cholera, RSV, Influenza-B, Novel-Pathogen, and Smallpox profiles have IFR/hospitalization curves derived from SARS-CoV-2 defaults, not pathogen-specific literature. For pathogen-specific outbreak investigation (e.g., real Ebola scenarios), users should override IFR/hospitalization curves manually or commission custom calibration. - **Initial immune fraction inflates "attack rate".** The generator places vaccinated + prior-infection agents in compartment R at day 0. The `attack_rate_cumulative` field therefore reflects (R + D) / N at any timestep — including the initial immune births. For "newly infected during simulation", subtract the day-0 R from the final R. - **Influenza-A simulation produces near-extinction dynamics by design.** R0=1.4 is barely above 1.0, so with any meaningful vaccination coverage the epidemic limps. This is correct epidemiological behavior — Influenza-A seasonal waves often have effective R0 near 1 due to partial population immunity from prior seasons. Use vax_coverage=0.0 for a more dramatic flu curve. - **Contact network records *transmission events only*, not the underlying contact graph.** Each row = one infection event (infector → infectee). The implicit contact graph (who-could-have-met-whom) is constructed at simulation time but not persisted, to keep file sizes tractable. The full product can optionally export the dense contact graph. - **Daily resolution.** This product simulates day-step dynamics. For sub-daily resolution (hourly, beat-to-beat) use specialized agent-based platforms (FRED, OpenABM, Covasim). - **No geographic mobility.** The full product simulates a single county (LA = FIPS 06037 by default). Multi-county metropolitan spread requires the full product's geographic routing extension. - **Synthetic, not derived from real surveillance.** Distributions match published CDC/WHO/POLYMOD references but do NOT reflect any specific real outbreak. --- ## Ethical use guidance This dataset is designed for: - Pandemic forecasting methodology development - Epidemiology research methodology - Contact tracing system testing - Public health AI pretraining - Educational use in epidemiology, biostatistics, and public health This dataset is **not appropriate for**: - Making public health policy decisions about real outbreaks - Real-world contact tracing or quarantine targeting - Vaccination prioritization for real populations without validation on real surveillance - Misinformation about specific real outbreaks (COVID-19, Ebola, etc.) - Discriminatory modeling targeting protected demographic groups --- ## Companion datasets in the Healthcare vertical - [HLT-001](https://huggingface.co/datasets/xpertsystems/hlt001-sample) — Synthetic Patient Population (CDC/NHANES) - [HLT-002](https://huggingface.co/datasets/xpertsystems/hlt002-sample) — Synthetic EHR (FHIR R4) - [HLT-003](https://huggingface.co/datasets/xpertsystems/hlt003-sample) — Synthetic Clinical Trial (3 endpoints + power) - [HLT-004](https://huggingface.co/datasets/xpertsystems/hlt004-sample) — Synthetic Disease Progression (longitudinal) - [HLT-005](https://huggingface.co/datasets/xpertsystems/hlt005-sample) — Synthetic Hospital Admission - [HLT-006](https://huggingface.co/datasets/xpertsystems/hlt006-sample) — Synthetic Medical Imaging (DICOM + COCO) - [HLT-007](https://huggingface.co/datasets/xpertsystems/hlt007-sample) — Synthetic Drug Response (PGx + PK) - [HLT-008](https://huggingface.co/datasets/xpertsystems/hlt008-sample) — Synthetic Healthcare Claims (X12 + fraud) - [HLT-009](https://huggingface.co/datasets/xpertsystems/hlt009-sample) — Synthetic ICU Vital Sign Monitoring - [HLT-010](https://huggingface.co/datasets/xpertsystems/hlt010-sample) — Synthetic Hospital Resource Usage - [HLT-011](https://huggingface.co/datasets/xpertsystems/hlt011-sample) — Synthetic Rare Disease + Trial Eligibility Engine - **HLT-012** — Synthetic Pandemic Spread Dataset (you are here) Use **HLT-001 through HLT-012 together** for the full healthcare data stack — and HLT-012 specifically extends the catalog into **population-level infectious disease dynamics**, complementing HLT-001 (population) and HLT-005 (hospital admissions) for end-to-end pandemic preparedness modeling. --- ## Citation If you use this dataset, please cite: ```bibtex @dataset{xpertsystems_hlt012_sample_2026, author = {XpertSystems.ai}, title = {HLT-012 Synthetic Pandemic Spread Dataset (Sample Preview)}, year = 2026, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/xpertsystems/hlt012-sample} } ``` --- ## Contact - **Web:** [https://xpertsystems.ai](https://xpertsystems.ai) - **Email:** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) - **Full product catalog:** Cybersecurity, Insurance & Risk, Materials & Energy, Oil & Gas, Healthcare, and more **Sample License:** CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) **Full product License:** Commercial — please contact for pricing.