MOOvPOPsurveillance incorporates real-world disease distribution and harvest heterogeneities, and can be used to simulate disease surveillance strategies under alternate assumptions. The model can be used to determine population-specific sample sizes for prompt detection of wildlife diseases like chronic wasting disease (CWD). MOOvPOPsurveillance is initialized with model-generated ( MOOvPOP: https://www.comses.net/codebases/5585/releases/2.2.0/ ) pre-harvest deer population snapshot (abundance, sex-age composition and distribution in the landscape) for selected sampling regions in Missouri. CWD+ deer are then distributed in the landscape under one of the two assumptions: 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).
Release Notes
MOOvPOPsurveillance v2.2.0: can read data directly from the ‘data’ folder and write result files in the ‘results’ folder.
Associated Publications
Belsare, A.V., Gompper, M.E., Keller, B.J., Sumners, J.A., Hansen, L.P., and Millspaugh, J.J. An agent-based framework for improving wildlife disease surveillance: A case study of chronic wasting disease in Missouri white-tailed deer. 2020. Ecological Modelling 417 (108919). (F1000Prime Recommended Article).
Belsare, A.V., Gompper, M.E., Keller, B.J., Sumners, J.A., Hansen, L.P., and Millspaugh, J.J. Size Matters: Sample size assessments for chronic wasting disease surveillance using an agent-based modeling framework. 2020. MethodsX 7(100953). https://doi.org/10.1016/j.mex.2020.100953.
Mysterud A, Viljugrein H, Rolandsen CM, Belsare AV. 2021 Harvest strategies for the elimination of low prevalence wildlife diseases. R. Soc. Open Sci. 8: 210124. https://doi.org/10.1098/rsos.210124
MOOvPOPsurveillance 2.2.0
Submitted byAniruddha BelsarePublished May 12, 2020
Last modified Apr 06, 2022
MOOvPOPsurveillance incorporates real-world disease distribution and harvest heterogeneities, and can be used to simulate disease surveillance strategies under alternate assumptions. The model can be used to determine population-specific sample sizes for prompt detection of wildlife diseases like chronic wasting disease (CWD). MOOvPOPsurveillance is initialized with model-generated ( MOOvPOP: https://www.comses.net/codebases/5585/releases/2.2.0/ ) pre-harvest deer population snapshot (abundance, sex-age composition and distribution in the landscape) for selected sampling regions in Missouri. CWD+ deer are then distributed in the landscape under one of the two assumptions: 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).
Release Notes
MOOvPOPsurveillance v2.2.0: can read data directly from the ‘data’ folder and write result files in the ‘results’ folder.
Cite this Model
Aniruddha Belsare, Matthew Gompper, Joshua J Millspaugh (2020, May 12). “MOOvPOPsurveillance” (Version 2.2.0). CoMSES Computational Model Library. Retrieved from: https://www.comses.net/codebases/5576/releases/2.2.0/
Associated Publication(s)
Belsare, A.V., Gompper, M.E., Keller, B.J., Sumners, J.A., Hansen, L.P., and Millspaugh, J.J. An agent-based framework for improving wildlife disease surveillance: A case study of chronic wasting disease in Missouri white-tailed deer. 2020. Ecological Modelling 417 (108919). (F1000Prime Recommended Article).
Belsare, A.V., Gompper, M.E., Keller, B.J., Sumners, J.A., Hansen, L.P., and Millspaugh, J.J. Size Matters: Sample size assessments for chronic wasting disease surveillance using an agent-based modeling framework. 2020. MethodsX 7(100953). https://doi.org/10.1016/j.mex.2020.100953.
Mysterud A, Viljugrein H, Rolandsen CM, Belsare AV. 2021 Harvest strategies for the elimination of low prevalence wildlife diseases. R. Soc. Open Sci. 8: 210124. https://doi.org/10.1098/rsos.210124
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