Simulated Data for Testing Disease Emergence Indicators

This dataset provides simulated data used to test generalizable approaches for anticipating infectious disease emergence. It supports two parallel analytical methods: a set of early-warning signals based on critical slowing down theory and a likelihood-based approach. The data was generated by Tobias S. Brett to validate indicators across a range of different model structures and parameter values.

Use Cases

• Test early-warning signals based on critical slowing down theory using simulated outbreak data.
• Validate likelihood-based approaches for anticipating disease emergence prior to large-scale outbreaks.
• Contrast theoretical predictions of disease emergence indicators with simulated data from various model structures.

Strengths

• Data is designed for general applicability, not specific to a single disease system or model.
• Methods were tested across a range of different model structures and parameter values.
• Dataset is openly available under a CC0 1.0 license for unrestricted use.

Limitations

• The dataset consists of simulated, not real-world, epidemiological data, limiting direct applicability.
• Specific details on data volume (rows), features (columns), and file formats are unknown.
• The temporal and geographic scope of the underlying simulations is not specified.

Provenance

Source

dryad

Collection Method

Data generated through simulation to test theoretical approaches for anticipating disease emergence.

Freshness

Last updated on 2020-06-24.