Sensitivity analysis (SA) is commonly used to investigate the influence of input parameter values on simulation model outcomes. In contrast with univariate SA, global SA (GSA) accounts for interactions and non-linear relationships between input parameters. An example is Sobol GSA, a variance-based GSA, rarely used for epidemiological models. Total and first effects Sobol indices (SI, [0,1]) are computed, describing the influence of input parameters with and without parameters interaction.We applied Sobol GSA to an agent-based, spatially explicit, stochastic rabies outbreak simulation model developed for a small free-roaming dog population in Northern Australia. Vaccination, culling and dog confinement were included as control strategies. We investigated the sensitivity of three model outcomes (outbreak size: number of rabid dogs (NRD) and number of dead dogs (died from rabies or culled, NDD); and outbreak duration (OD)) to core model parameters and parameters specifying control interventions.
|Number of pages||2|
|Publication status||Published - Nov 2017|
|Event||Modelling in Animal Health : ModAH conference - Ecole d'Architecture, Nantes, France|
Duration: 14 Jun 2017 → 16 Jun 2017
|Conference||Modelling in Animal Health|
|Period||14/06/17 → 16/06/17|