Bayesian Posterior Inference Results for Hospital and Community Transmission Scenarios

A summary of Bayesian posterior inference results from 100 simulation replicates for different pathogen transmission scenarios. The dataset, created by Sanni Översti and last updated in March 2026, includes metrics like effective sample size, relative error, relative bias, and 95% highest posterior density accuracy for parameters in hospital-driven, equal, and community-driven transmission models. It is a small dataset of 9.5 KB, stored in an XLS file format.

Use Cases

• Validate Bayesian inference methods based on simulation results for different transmission scenarios.
• Compare parameter estimation accuracy (relative error and bias) between hospital-driven and community-driven transmission models.
• Assess the reliability of 95% highest posterior density intervals for epidemiological parameters.
• Benchmark computational performance based on effective sample size metrics across 100 simulation replicates.

Strengths

• Results are derived from 100 simulation replicates per scenario, providing a basis for statistical assessment.
• Includes multiple performance metrics: effective sample size, relative error, relative bias, and HPD interval accuracy.
• Released under a permissive CC-BY-4.0 license, allowing for broad reuse.

Limitations

• The dataset is very small (9.5 KB), indicating limited scope and likely a summary of results rather than raw simulation data.
• Column-level documentation is absent; field semantics must be inferred from the description after download.
• Row count is unknown, which may limit suitability assessment for certain analyses.

Provenance

Source

figshare, author Sanni Översti.

Collection Method

Likely contains summarized results from computational simulations of epidemic dynamics.

Freshness

Last updated 2026-03-17 16:41:45; freshness should be verified.