Compartmental Model Parameters for Simulating Measles Outbreak Detection

Stochastic SEIR model parameters simulate daily incidence of measles and non-measles febrile rash illness to evaluate outbreak detection systems. The 5.5 KB Excel file, authored by Callum R. K. Arnold and last updated in April 2026, models the impact of diagnostic test accuracy and background noise on alert thresholds. This simulation-based dataset likely contains parameters for generating synthetic epidemiological time-series data.

Use Cases

• Calibrating outbreak detection thresholds based on simulated diagnostic test accuracy and testing rates described in the methodology.
• Evaluating the performance of rapid diagnostic tests (RDTs) against perfect tests under different background noise structures (static vs. dynamical).
• Analyzing the sensitivity of outbreak detection accuracy to the magnitude and structure of background noise, as discussed in the conclusions.

Strengths

• Model parameters are explicitly defined for simulating measles and non-measles (e.g., rubella) incidence using an SEIR framework.
• The dataset is licensed under CC-BY-4.0, permitting open sharing and adaptation.
• The description provides a detailed methodological context for the simulated data generation.

Limitations

• The dataset is very small at 5.5 KB, indicating a limited scope of parameters rather than extensive simulation outputs.
• 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 statistical modeling.

Provenance

Source

figshare

Collection Method

Parameters for a stochastic SEIR model generated to simulate disease incidence.

Freshness

Last updated 2026-04-29 17:51:28; freshness should be verified.