Slope Estimates for COVID-19 and Influenza Hospital Admission Forecastability, 2022-2024

Slope estimates from linear regression models analyzing the relationship between forecastability and population size for COVID-19 and influenza hospital admissions. The 5.5 KB dataset, authored by Lauren A. White, contains results from U.S. state and national HHS/NHSN data for the 2022-2023 and 2023-2024 seasons. Values are presented with three significant figures, with rows statistically significant at p=0.05 indicated.

Use Cases

• Compare forecastability trends between COVID-19 and influenza based on the described regression slopes.
• Analyze the effect of daily versus weekly reporting frequency on forecastability metrics.
• Investigate the relationship between population size and forecastability for infectious disease admissions.
• Validate statistical significance of forecastability models using the provided p-value threshold.

Strengths

• Data covers two distinct respiratory disease seasons (2022-2023 and 2023-2024).
• Statistical significance is explicitly marked for results at a p=0.05 threshold.
• Values are reported with three significant figures, suggesting precision.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 5.5 KB size indicates a very limited scope, likely containing only summary statistics.

Provenance

Source

U.S. Department of Health and Human Services (HHS) and National Healthcare Safety Network (NHSN) data.

Collection Method

Linear regression model fits applied to hospital admission data.

Time Range

2022-2023 and 2023-2024 seasons

Freshness

Last updated 2026-04-15 17:57:24; freshness should be verified.

Geography

United States (state and national level)