Chronic Wasting Disease Surveillance Optimization Software
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Hanley, Brenda J.; Mitchell, Corey I.; Walter, W. David; Walsh, Daniel; Jennelle, Christopher; Hollingshead, Nicholas; Abbott, Rachel C.; Kelly, James; Grove, Daniel; Williams, David; Them, Cara; Ahmed, Md Sohel; Miller, Lauren; Schuler, Krysten L.
The Chronic Wasting Disease Surveillance Optimization Software computes sampling strategy recommendations for state-wise use of surveillance funds by a state wildlife management agency when the goal of the disease surveillance program is to detect chronic wasting disease (CWD) in white-tailed deer (Odocoileus virginianus). Driven by a combinatorial optimization algorithm, the Chronic Wasting Disease Surveillance Optimization Software pinpoints the combination of samples that should be tested in each population segment of deer (adult male, adult female, yearling male, yearling female, fawn male, fawn female), in each collection category of deer (hunter harvested, non-hunter harvested), and in each county (or other administrative area) of the state to maximize the return-on-investment, while keeping the overall surveillance program within the predetermined budget. Inputs to the combinatorial optimization algorithm in the Chronic Wasting Disease Surveillance Optimization Software broadly include the Optimization Matrix and the Historical Data. The Optimization Matrix includes information such as the costs, benefits, and starting prevalence of each population segment, collection category, and county or other administrative unit. The Historical Data considers the previously enacted sampling strategy in each county (or other administrative area), then uses that data as comparison to the algorithmic recommendations. Agencies may further parameterize their algorithm to achieve specific management objectives. Objectives include monitoring known infections, searching for new infections, and providing detailed information to the public. The Chronic Wasting Disease Surveillance Optimization Software includes (redacted) Optimization Matrices and (redacted) Historical Data from Alabama, Arkansas, Connecticut, Florida, Georgia, Indiana, Iowa, Kentucky, Louisiana, Maryland, Michigan, Minnesota, Mississippi, New Hampshire, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, Tennessee, Virginia, and Wisconsin, US, but the software may be adapted for use in other states and provinces.
This software is being shared under a MIT license. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Financial support comes from (1) Michigan Disease Initiative –Optimizing CWD Surveillance: Regional Synthesis of Demographic, Spatial, and Transmission-Risk Factors (2019); (2) Tennessee Wildlife Resources Agency -Modeling Risk of Infection for Individually Harvested Deer & Estimating Prevalence When Sampling is Limited (2020); (3) Michigan Disease Initiative-SOP4CWD Dashboard: A Web Application for Disease Visualization and Data-Driven Decisions (2020); (4) Multistate Conservation Grant Program -Surveillance Optimization Project for Chronic Wasting Disease: Streamlining a Web Application for Disease Visualization and Data-Driven Decisions (2021).
Surveillance quotas; chronic wasting disease; white-tailed deer