Challenges in Estimating Time-Varying Epidemic Severity Rates from Aggregate Data.

Shioda, K. (2024). Challenges in Estimating Time-Varying Epidemic Severity Rates from Aggregate Data.

Type

Preprint

Intellectual base

Grey literature

Year

2024

DOI

10.1101/2024.12.27.24319518v1 did not resolve

DOI registrant

Cold Spring Harbor Laboratory

Link

https://www.medrxiv.org/content/10.1101/2024.12.27.24319518v1

Cited from

statistics_experimental_design

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Primes

• Aggregate-Marginal Divergence
  • Epidemiology and healthcare. Total case counts fall while case-fatality in the most vulnerable subgroup rises, or hospital-wide readmission falls while the highest-acuity stratum worsens.

    This sourceShows that severity rates (e.g., case-fatality) estimated from ratios of aggregate counts carry large biases and can fail to detect heightened risk in shifting subpopulations — supporting that aggregate case counts mask a reversed marginal/per-cohort vulnerability trend, and that per-cohort/per-wave measurement is the repair.

    Supported in partVerified against the work's full text

    Shows that severity rates estimated from ratios of aggregate counts carry large statistical biases and may fail to detect heightened risks, supporting the masking mechanism but not the readmission example.

    “In practice, time-varying severity rates are typically estimated using a ratio of aggregate counts. We demonstrate that these estimators are capable of exhibiting large statistical biases, with concerning implications for public health practice, as they may fail to detect heightened risks or falsely signal nonexistent surges.”

    Read by an automated reader; not a human review. How support was checked

Verification

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