Bayesian Posterior Inference Results for Hospital and Community Transmission Scenarios

A summary of Bayesian posterior inference results from 100 simulation replicates for three transmission scenarios: hospital-driven, equal, and community-driven. The dataset includes metrics on effective sample size, relative error, relative bias, and 95% highest posterior density interval accuracy for epidemiological parameters. It was authored by Sanni Översti and uploaded to figshare on March 17, 2026.

Use Cases

• Compare parameter estimation accuracy across different transmission scenarios (HDT, ET, CDT) based on the relative error and bias metrics.
• Assess computational reliability of Bayesian inference based on the count of replicates with sufficient effective sample size (ESS).
• Validate 95% highest posterior density (HPD) interval coverage for epidemic parameters based on the reported HPD accuracy column.

Strengths

• Results are based on 100 simulation replicates per scenario, providing a basis for statistical assessment.
• Includes multiple diagnostic and accuracy metrics: effective sample size (ESS), relative error, relative bias, and HPD interval accuracy.

Limitations

• The community sampling rate is unspecified in the provided description, which is a key simulation parameter.
• Row count and specific column names are unknown, limiting pre-download assessment of data structure.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Results from computational simulations of transmission dynamics.

Freshness

Last updated 2026-03-17 16:41:44; freshness should be verified.