xpertsystems/hlt012-sample

HLT-012 — Synthetic Pandemic Spread Dataset (Sample Preview)

A free, schema-identical preview of the full HLT-012 commercial product from XpertSystems.ai.

A fully synthetic mechanistic SEIRD agent-based epidemic simulation dataset combining daily compartment dynamics (Susceptible / Exposed / Infectious / Recovered / Deceased), agent-level contact networks with transmission events, variant emergence tracking, vaccination + waning immunity, NPI response dampening, and Rt estimation via renewal equation — calibrated to CDC / WHO / POLYMOD / Levin 2022 benchmarks across 12 pathogen profiles.

⚠️ 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.

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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.

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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.

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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).

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Loading examples

Pandas — epidemic curve plot

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

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

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

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)

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

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)

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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

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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.

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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.

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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

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Companion datasets in the Healthcare vertical

• HLT-001 — Synthetic Patient Population (CDC/NHANES)
• HLT-002 — Synthetic EHR (FHIR R4)
• HLT-003 — Synthetic Clinical Trial (3 endpoints + power)
• HLT-004 — Synthetic Disease Progression (longitudinal)
• HLT-005 — Synthetic Hospital Admission
• HLT-006 — Synthetic Medical Imaging (DICOM + COCO)
• HLT-007 — Synthetic Drug Response (PGx + PK)
• HLT-008 — Synthetic Healthcare Claims (X12 + fraud)
• HLT-009 — Synthetic ICU Vital Sign Monitoring
• HLT-010 — Synthetic Hospital Resource Usage
• HLT-011 — 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.

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Citation

If you use this dataset, please cite:

@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}
}

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Contact

• Web: https://xpertsystems.ai
• Email: 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.