---
id: "uk-covid-nation-1dcumulative"
slug: "uk-covid-nation-1dcumulative"
name: "UK COVID-19 National Epidemiological Surveillance"
domain: "Healthcare & Epidemiology"
taskType: "time_series_forecasting"
numRows: 2944
numColumns: 16
targetColumn: "cumulative_admissions"
primaryMetric: "Timestral-123M SQL"
primaryScore: "6.4620"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/uk-covid-nation-1dcumulative/dataset.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/1A9_I3pqml_FnbueWwq4Gk4qSdBu_x4Av-FmrMQfJ2G8/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/uk-covid-nation-1dcumulative"
---

# Dataset: UK COVID-19 National Epidemiological Surveillance (uk-covid-nation-1dcumulative)

> Multi-series national epidemiological surveillance tracking 4 UK nations at daily cadence over a 28-day forecast horizon to project cumulative hospital admissions, infections, and mortality alongside critical ICU capacity constraints.

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `time_series_forecasting` |
| **Business Domain** | Healthcare & Epidemiology |
| **Target Column** | `cumulative_admissions` |
| **Rows (Instances)** | 2,944 |
| **Features (Columns)** | 16 |
| **Primary Evaluation Metric** | Timestral-123M SQL |
| **Best Benchmark Score** | **6.4620** |
| **Prediction Horizon** | 28 steps |
| **Seasonality** | 1 |
| **Time Frequency** | Daily |
| **Timestamp Column** | `timestamp` |
| **Entity / ID Column** | `id` |

## Business Decision Context
**Context:** National health services (NHS England, Scotland, Wales, and Northern Ireland) operate under centralized resource orchestration, requiring rolling monthly projections for acute care bed allocation, staff surge scheduling, and critical care medication procurement during shifting epidemic waves.
**Objective:** Forecast 28 days ahead for cumulative hospital admissions, cumulative confirmed cases, and cumulative deaths across all 4 devolved nations, utilizing past vaccination rollouts and inpatient bed pressure.
**Decision Impact:** Under-forecasting risks critical care collapse, regional ICU triage, and emergency patient diversion, with severe excess mortality. Over-forecasting leads to unnecessary cancellation of elective surgeries, prolonged diagnostic backlogs, and costly surge capacity stand-by overhead.

## Feature Schema
| Column Name | Data Type | Role | Distinct Values | Missing Values (%) | Description |
| :--- | :--- | :--- | :--- | :--- | :--- |
| `id` | `categorical` | identifier | 4 | 0 | Identifier denoting the UK devolved nation (England, Northern Ireland, Scotland, Wales) |
| `timestamp` | `datetime` | timestamp | 757 | 0 | Reporting calendar date for the surveillance metrics |
| `cumulative_admissions` | `numeric` | target | 2,804 | 85 | Total cumulative count of COVID-19 positive patients admitted to secondary care hospitals |
| `cumulative_cases` | `numeric` | target | 2,879 | 0 | Running aggregate of confirmed positive SARS-CoV-2 PCR and rapid antigen test episodes |
| `cumulative_deaths` | `numeric` | target | 2,377 | 84 | Total cumulative deaths occurring within 28 days of a positive laboratory test |
| `cumulative_vaccinated_1` | `numeric` | past_covariate | 1,627 | 1300 | Cumulative total of first vaccine doses administered to the eligible population |
| `cumulative_vaccinated_2` | `numeric` | past_covariate | 1,630 | 1300 | Cumulative total of completed primary 2-dose vaccination courses administered |
| `cumulative_vaccinated_3` | `numeric` | past_covariate | 549 | 2386 | Cumulative total of third booster vaccinations administered across the nation |
| `hospital_cases` | `numeric` | past_covariate | 1,620 | 107 | Daily census of confirmed COVID-19 patients actively occupying general acute hospital beds |
| `icu_ventilator_occupancy` | `numeric` | past_covariate | 633 | 133 | Daily midnight census count of confirmed COVID-19 patients on invasive mechanical ventilators |
| `new_cases` | `numeric` | past_covariate | 1,970 | 0 | Newly reported positive COVID-19 infections published on the reporting date |
| `new_deaths` | `numeric` | past_covariate | 346 | 84 | Newly certified deaths within 28 days of positive test reported on the surveillance date |
| `new_admissions` | `numeric` | past_covariate | 661 | 85 | Number of newly admitted COVID-19 patients across hospital trusts during the 24-hour cycle |
| `new_vaccinated_1` | `numeric` | past_covariate | 1,528 | 1304 | Total count of first vaccine doses administered on the given date |
| `new_vaccinated_2` | `numeric` | past_covariate | 1,519 | 1304 | Total count of second course vaccinations completed on the given date |
| `new_vaccinated_3` | `numeric` | past_covariate | 545 | 2390 | Total count of booster doses injected during the recording window |

## FEV Benchmark Leaderboard (AutoGluon Time-Series Suite)
Empirical probabilistic evaluation across standard foundation models and baselines:

| Rank | Method / Model | Family | Organization | SQL Win Rate (%) | SQL Skill Score | MASE Win Rate (%) | Median Latency | Execution |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | **Timestral-209M** | Foundation Model | — | 87.7% | 50.39 | 86.0% | 0.51s | Server (CUDA / PyTorch) |
| 2 | **Timestral-123M (Carla Engine)** | Foundation Model | — | 86.8% | 50.18 | 85.0% | 0.32s | WebGPU (Browser / Sheets) |
| 3 | **TimeRSI** | Closed-Source API | — | 84.8% | 51.24 | 83.8% | 2512s | Server (CUDA / PyTorch) |
| 4 | **t0-gamma** | Foundation Model | — | 84.4% | 49.42 | 81.8% | 4.38s | Server (CUDA / PyTorch) |
| 5 | **Timestral-28M** | Foundation Model | — | 79.2% | 48.42 | 77.0% | 0.11s | Server (CUDA / PyTorch) |
| 6 | **timesfm-3** | Foundation Model | — | 78.5% | 48.99 | 77.0% | 3.41s | Server (CUDA / PyTorch) |
| 7 | **ChakraTS** | Baseline | — | 78.2% | 48.14 | 79.5% | 14.22s | Server (CUDA / PyTorch) |
| 8 | **Lingjiang2.0** | Baseline | — | 73.9% | 47.02 | 71.3% | 8.89s | Server (CUDA / PyTorch) |
| 9 | **Chronos-2** | Foundation Model | — | 70.4% | 47.28 | 68.4% | 0.84s | Server (CUDA / PyTorch) |
| 10 | **timeh2o-v1-tiny** | Foundation Model | — | 69.9% | 45.49 | 67.0% | 0.19s | Server (CUDA / PyTorch) |
| 11 | **Timestral-5M** | Foundation Model | — | 69.0% | 46.19 | 67.7% | 0.04s | Server (CUDA / PyTorch) |
| 12 | **Toto-2.0-2.5B** | Baseline | — | 67.5% | 44.42 | 65.7% | 5.58s | Server (CUDA / PyTorch) |
| 13 | **t0-beta** | Foundation Model | — | 67.4% | 46.65 | 64.5% | 2.29s | Server (CUDA / PyTorch) |
| 14 | **Toto-2.0-1B** | Baseline | — | 66.4% | 44.43 | 65.5% | 2.48s | Server (CUDA / PyTorch) |
| 15 | **TiRex-2** | Foundation Model | — | 65.8% | 45.50 | 63.4% | 0.27s | Server (CUDA / PyTorch) |

## Provenance & Downloadable Artifacts
- **Dataset Parquet File:** [Direct Parquet Download](https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/uk-covid-nation-1dcumulative/dataset.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/1A9_I3pqml_FnbueWwq4Gk4qSdBu_x4Av-FmrMQfJ2G8/edit?usp=sharing)
- **Academic Paper / Citation:** [Read Research Paper](https://coronavirus.data.gov.uk/)
- **Canonical HTML Page:** [https://carlahq.eu/datasets/uk-covid-nation-1dcumulative](https://carlahq.eu/datasets/uk-covid-nation-1dcumulative)