metadata
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<n<10K
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.
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
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)
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_hospitalizationsandicu_occupancytrajectories 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'sCHANGELOG. - 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_cumulativefield 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 — 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.
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}
}
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.