Logistic Regression Models for COVID-19 Hospitalization and Fatality Risk

Logistic regression models of COVID-19 case hospitalisation and case fatality. The dataset was authored by Christopher Standen and last updated on 3 April 2026. It is a 9.5 KB XLS file available under a CC-BY-4.0 license on figshare.

Use Cases

• Modeling the probability of hospitalization for COVID-19 cases based on described risk factors.
• Estimating case fatality odds ratios for COVID-19 using logistic regression.
• Analyzing the association between socioeconomic disadvantage and severe COVID-19 outcomes as suggested by platform tags.
• Studying the impact of public health responses on hospitalization and fatality rates.

Strengths

• Dataset is openly licensed (CC-BY-4.0).
• File size is small (9.5 KB), facilitating quick download and inspection.
• Platform tags suggest the inclusion of adjusted odds ratios, a key statistical measure.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Christopher Standen via figshare.

Collection Method

Likely contains results from a retrospective cohort study, as indicated by platform tags.

Time Range

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Freshness

Last updated 2026-04-03 17:53:34; freshness should be verified.

Geography

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