Computational Model Library

MOOvPOPsurveillance (1.6.0)

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).

MOOvPOPsurveillance_v2.2.0 interface.png

Release Notes

Output file is created in the same folder where MOOvPOPsurveillance_v2 is located.
Parameters with constant values are now simulated using reporters.

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

This release is out-of-date. The latest version is 2.2.0

MOOvPOPsurveillance 1.6.0

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

Output file is created in the same folder where MOOvPOPsurveillance_v2 is located.
Parameters with constant values are now simulated using reporters.

Version Submitter First published Last modified Status
2.2.0 Aniruddha Belsare Tue May 12 16:37:24 2020 Tue May 12 16:37:25 2020 Published
2.1.2 Aniruddha Belsare Thu Aug 8 21:31:35 2019 Wed Apr 6 19:20:20 2022 Published https://doi.org/10.25937/8hpz-9y96
1.8.0 Aniruddha Belsare Thu Jan 18 22:35:11 2018 Wed Sep 18 07:07:51 2019 Published Peer Reviewed
1.7.0 Aniruddha Belsare Mon Nov 27 02:22:12 2017 Tue Feb 20 09:52:34 2018 Published
1.6.0 Aniruddha Belsare Sun Nov 26 23:19:23 2017 Tue Feb 20 09:52:38 2018 Published
1.5.0 Aniruddha Belsare Sun Nov 26 23:12:05 2017 Tue Feb 20 09:52:41 2018 Published
1.4.0 Aniruddha Belsare Sun Nov 26 22:30:19 2017 Tue Feb 20 09:52:43 2018 Published
1.3.0 Aniruddha Belsare Mon Nov 6 18:55:51 2017 Tue Feb 20 09:52:49 2018 Published
1.2.0 Aniruddha Belsare Mon Aug 14 04:52:53 2017 Tue Feb 20 09:52:47 2018 Published
1.1.0 Aniruddha Belsare Tue Apr 4 21:03:28 2017 Tue Feb 20 09:52:52 2018 Published
1.0.0 Aniruddha Belsare Tue Apr 4 17:03:40 2017 Tue Feb 20 09:52:56 2018 Published

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