MOOvPOPsurveillance incorporates real-world sampling biases and disease distribution heterogeneities, and provides population-specific recommendations for collection and analysis of disease surveillance data obtained from hunter-harvested animals (or other non-probabilistic sampling methods). MOOvPOPsurveillance is formulated to simulate hunter harvest and CWD testing under different assumptions. The model is initialized by importing model-generated pre-harvest deer population data (abundance, sex-age composition and distribution in the landscape) for a sampling region in Missouri. CWD+ deer are then distributed in the landscape under one of the two assumption: random or clustered distribution. User selects the sampling region, age-sex class wise distribution of CWD prevalence, age-sex class wise sample sizes (proportion of harvest tested) and sampling method (random or non-random). Three processes are implemented: 1) individual growth (age of every deer increases by one month), 2) non-hunting mortality (determined by age- and sex- specific monthly mortality rates), and 3) hunting mortality and CWD testing. MOOvPOPsurveillance runs for one time-step (one month), and provides following outputs: total number of adult deer (male and female) remaining in the population after harvest, number of CWD+ deer in the population, in the hunter harvest, and in the sample (deer tested for CWD).
This is a companion discussion topic for the original entry at https://www.comses.net/codebases/5576/releases/1.8.0/?platform=hootsuite