README.md

---
license: cc-by-4.0
pretty_name: SpatialEpiBench
tags:
- tabular
- timeseries
- time-series
- geospatial
- epidemiology
- spatial-epidemiology
- epidemic-forecasting
- public-health
- benchmark
- csv
- graph
- spatiotemporal
- mlcroissant
task_categories:
- time-series-forecasting
viewer: true
configs:
- config_name: AUcase
  data_files:
  - split: train
    path: AUcase.csv
- config_name: AUcase_adj
  data_files:
  - split: train
    path: AUcase_adj.csv
- config_name: CANpositivity
  data_files:
  - split: train
    path: CANpositivity.csv
- config_name: CANpositivity_adj
  data_files:
  - split: train
    path: CANpositivity_adj.csv
- config_name: CAcase
  data_files:
  - split: train
    path: CAcase.csv
- config_name: CAcase_adj
  data_files:
  - split: train
    path: CAcase_adj.csv
- config_name: CHNGinpatient
  data_files:
  - split: train
    path: CHNGinpatient.csv
- config_name: CHNGinpatient_adj
  data_files:
  - split: train
    path: CHNGinpatient_adj.csv
- config_name: CHNGoutpatient
  data_files:
  - split: train
    path: CHNGoutpatient.csv
- config_name: CHNGoutpatient_adj
  data_files:
  - split: train
    path: CHNGoutpatient_adj.csv
- config_name: CPRadmissions
  data_files:
  - split: train
    path: CPRadmissions.csv
- config_name: CPRadmissions_adj
  data_files:
  - split: train
    path: CPRadmissions_adj.csv
- config_name: DVcli
  data_files:
  - split: train
    path: DVcli.csv
- config_name: DVcli_adj
  data_files:
  - split: train
    path: DVcli_adj.csv
- config_name: HHShosp
  data_files:
  - split: train
    path: HHShosp.csv
- config_name: HHShosp_adj
  data_files:
  - split: train
    path: HHShosp_adj.csv
- config_name: ILI2019
  data_files:
  - split: train
    path: ILI2019.csv
- config_name: ILI2019_adj
  data_files:
  - split: train
    path: ILI2019_adj.csv
- config_name: JHUcase
  data_files:
  - split: train
    path: JHUcase.csv
- config_name: JHUcase_adj
  data_files:
  - split: train
    path: JHUcase_adj.csv
- config_name: NCHSdeaths
  data_files:
  - split: train
    path: NCHSdeaths.csv
- config_name: NCHSdeaths_adj
  data_files:
  - split: train
    path: NCHSdeaths_adj.csv
---

# SpatialEpiBench

## Dataset Summary

SpatialEpiBench is a benchmark collection of 11 spatiotemporal epidemic forecasting datasets. The benchmark covers multiple public-health surveillance modalities, including influenza-like illness surveillance rates, confirmed cases, test positivity, inpatient and outpatient hospitalizations, hospital admissions, doctor visits, and deaths. The datasets span the United States, Canada, and Australia, with daily or weekly temporal resolution depending on the data source.

Each dataset has been preprocessed to handle reporting backfill and source-data versioning. For outbreak-specific evaluation, the benchmark applies the outbreak annotation method from LRTrend to identify time intervals in each region where epidemic measurements are significantly rising. Metrics can then be recomputed specifically within these outbreak intervals to measure outbreak-period forecasting performance.

The repository provides each time-series dataset as a CSV file, paired with a corresponding spatial adjacency matrix in a `_adj.csv` file.

## Dataset Overview

| Dataset | Frequency | Country | Modality | Time |
|---|---|---|---|---|
| ILINet / `ILI2019` | weekly | U.S. | surveillance rate | 2010-present |
| `JHUcase` | daily | U.S. | cases | 2020-2023 |
| `CANpositivity` | daily | U.S. | test positivity | 2020-2021 |
| `CHNGinpatient` | daily | U.S. | inpatient hospitalizations | 2020-2024 |
| `CHNGoutpatient` | daily | U.S. | outpatient visits | 2020-2024 |
| `CPRadmissions` | daily | U.S. | hospital admissions | 2020-2023 |
| `DVcli` | daily | U.S. | doctor visits | 2020-present |
| `NCHSdeaths` | weekly | U.S. | deaths | 2020-present |
| `HHShosp` | daily | U.S. | hospitalizations | 2021-2024 |
| `CAcase` | daily | Canada | cases | 2020-2021 |
| `AUcase` | daily | Australia | cases | 2020-2021 |

Note: the ILINet dataset is stored in this repository using the filename/configuration name `ILI2019`.

## Repository Files

Each epidemiological time-series file is paired with an adjacency file:

| Time-series file | Adjacency file | Description |
|---|---|---|
| `AUcase.csv` | `AUcase_adj.csv` | Daily Australian case counts and spatial adjacency. |
| `CAcase.csv` | `CAcase_adj.csv` | Daily Canadian case counts and spatial adjacency. |
| `CANpositivity.csv` | `CANpositivity_adj.csv` | Daily U.S. test positivity measurements and spatial adjacency. |
| `CHNGinpatient.csv` | `CHNGinpatient_adj.csv` | Daily U.S. inpatient hospitalization measurements and spatial adjacency. |
| `CHNGoutpatient.csv` | `CHNGoutpatient_adj.csv` | Daily U.S. outpatient visit measurements and spatial adjacency. |
| `CPRadmissions.csv` | `CPRadmissions_adj.csv` | Daily U.S. hospital admission measurements and spatial adjacency. |
| `DVcli.csv` | `DVcli_adj.csv` | Daily U.S. doctor-visit measurements and spatial adjacency. |
| `HHShosp.csv` | `HHShosp_adj.csv` | Daily U.S. hospitalization measurements and spatial adjacency. |
| `ILI2019.csv` | `ILI2019_adj.csv` | Weekly U.S. ILINet surveillance-rate measurements and spatial adjacency. |
| `JHUcase.csv` | `JHUcase_adj.csv` | Daily U.S. case counts and spatial adjacency. |
| `NCHSdeaths.csv` | `NCHSdeaths_adj.csv` | Weekly U.S. death measurements and spatial adjacency. |

## Data Format

### Time-series CSV files

The main `*.csv` files contain regional epidemic time series.

Typical structure:

| Field | Type | Description |
|---|---|---|
| `time_value` | date/string | Observation date or epidemiological week. |
| Region columns | integer/float | Epidemic measurement for the corresponding region at `time_value`. |

Example:

```text
time_value,Region_1,Region_2,Region_3,...
2020-04-01,...
2020-04-02,...
```

### Adjacency CSV files

The `_adj.csv` files contain spatial adjacency matrices for the regions in the corresponding time-series dataset.

Typical structure:

| Field | Type | Description |
|---|---|---|
| First column | string | Row region name or region identifier. |
| Region columns | integer | Binary spatial adjacency indicator. `1` indicates adjacency/connection; `0` indicates no direct adjacency. |

Example:

```text
,Region_1,Region_2,Region_3,...
Region_1,0,1,0,...
Region_2,1,0,1,...
```

For cleaner schema inference, users may rename the first unnamed adjacency column to `region` after loading.

## Loading the Dataset

### Load with `datasets`

Each CSV is exposed as a separate Hugging Face configuration.

```python
from datasets import load_dataset

cases = load_dataset("ruiqil/SpatialEpiBench", "JHUcase")
adj = load_dataset("ruiqil/SpatialEpiBench", "JHUcase_adj")

print(cases["train"][0])
print(adj["train"][0])
```

### Load with `pandas`

```python
import pandas as pd

cases = pd.read_csv("hf://datasets/ruiqil/SpatialEpiBench/JHUcase.csv")
adj = pd.read_csv("hf://datasets/ruiqil/SpatialEpiBench/JHUcase_adj.csv", index_col=0)
```

## Intended Uses

SpatialEpiBench is intended for research on:

- spatiotemporal epidemic forecasting;
- spatial and graph-based public-health modeling;
- forecasting during outbreak growth periods;
- benchmarking models across multiple disease-activity modalities;
- evaluating temporal models with and without spatial adjacency information;
- comparing performance across countries, geographic scales, and surveillance targets.

## Out-of-Scope Uses

SpatialEpiBench should not be used as the sole basis for:

- clinical diagnosis;
- individual-level risk assessment;
- real-time public-health decision-making;
- emergency response decisions;
- policy decisions without additional validation and expert review.

The datasets are aggregated public-health time series intended for research benchmarking, not operational surveillance.

## Dataset Creation and Processing

The benchmark considers 11 spatiotemporal epidemic forecasting datasets. Each dataset has been appropriately preprocessed to handle reporting backfill and data-source versioning.

For outbreak-specific evaluation, outbreak intervals are annotated using the LRTrend outbreak annotation method. This method identifies region-specific time intervals during which epidemic measurements are significantly rising. Forecasting metrics can then be recomputed within those intervals to evaluate outbreak-specific performance.

Full dataset statistics, including outliers, zeros, and outbreak intervals, are reported in the associated benchmark paper or appendix.

## Source Data

The benchmark draws from established epidemic and public-health data sources, including:

- U.S. ILINet influenza-like illness surveillance data;
- Johns Hopkins University COVID-19 case data;
- Delphi-style COVID-19 and public-health surveillance data sources;
- NCHS mortality data;
- hospitalization and healthcare-utilization data sources.

Please refer to the associated paper for the exact source references, extraction dates, and preprocessing details for each dataset.

## Limitations

Users should consider the following limitations:

- Public-health time series may contain reporting delays, backfill, missing values, source revisions, and reporting artifacts.
- Surveillance definitions, reporting practices, and healthcare-seeking behavior vary across time, regions, and data sources.
- Daily datasets may contain weekday/weekend effects.
- Weekly datasets may use epidemiological weeks rather than calendar weeks.
- Spatial adjacency matrices encode geographic neighborhood structure and may not capture mobility, commuting, travel, demographic similarity, or healthcare referral patterns.
- Aggregated regional data can mask substantial within-region heterogeneity.
- Outbreak-period annotations are useful for benchmark evaluation, but they should not be interpreted as definitive epidemiological event labels.

## Biases and Responsible Use

Potential sources of bias include differences in testing availability, reporting coverage, case definitions, healthcare access, surveillance intensity, hospitalization practices, and death certification. Models trained on this dataset may learn patterns from reporting systems as well as from epidemic dynamics.

Researchers should report uncertainty, validate findings across multiple datasets and regions, and avoid overclaiming causal or operational conclusions.

## Personal and Sensitive Information

SpatialEpiBench contains aggregated regional public-health time series. It is not intended to contain individual-level records or personally identifiable information.

## License

This dataset is released under the Creative Commons Attribution 4.0 International license (`CC-BY-4.0`).

Users must provide appropriate attribution when using or redistributing this dataset.

### Initial release

- Added 11 spatiotemporal epidemic forecasting datasets.
- Added corresponding spatial adjacency matrices.
- Added Hugging Face dataset configurations for each CSV file.