DOI: 10.5061/dryad.v23517k
title: Data from: Not all surveillance data are created equal – a multi‐method dynamic occupancy approach to determine rabies elimination from wildlife

description: 1. A necessary component of elimination programs for wildlife disease is
 effective surveillance. The ability to distinguish between disease freedom
 and non‐detection can mean the difference between a successful elimination
 campaign and new epizootics. Understanding the contribution of different
 surveillance methods helps to optimize and better allocate effort and
 develop more effective surveillance programs. 2.We evaluated the
 probability of rabies virus elimination (disease freedom) in an enzootic
 area with active management using dynamic occupancy modeling of 10 years
 of raccoon rabies virus (RABV) surveillance data (2006‐2015) collected
 from three states in the eastern USA. We estimated detection probability
 of RABV cases for each surveillance method (e.g., strange acting reports,
 road‐kill, surveillance‐trapped animals, nuisance animals, and public
 health samples) used by the USDA National Rabies Management Program.
 3.Strange acting, found dead, and public health animals were the most
 likely to detect RABV when it was present, and generally detectability was
 higher in the fall‐winter compared to the spring‐summer. Found dead
 animals in the fall‐winter had the highest detection at 0.33 (95% CI:
 0.20, 0.48). Nuisance animals had the lowest detection probabilities
 (~0.02). 4.Areas with oral rabies vaccination (ORV) management had reduced
 occurrence probability compared to enzootic areas without ORV management.
 RABV occurrence was positively associated with deciduous and mixed forests
 and medium to high developed areas, which are also areas with higher
 raccoon (Procyon lotor) densities. By combining occupancy and detection
 estimates we can create a probability of elimination surface that can be
 updated seasonally to provide guidance on areas managed for wildlife
 disease. 5.Synthesis and applications. Wildlife disease surveillance is
 often comprised of a combination of targeted and convenience‐based
 methods. Using a multi‐method analytical approach allows us to compare the
 relative strengths of these methods, providing guidance on resource
 allocation for surveillance actions. Applying this multi‐method approach
 in conjunction with dynamic occupancy analyses better informs management
 decisions by understanding ecological drivers of disease occurrence.
description: Surveillance data by
 grid ID, season, and surveillance methodThis file contains
 positive and negative rabies surveillance data by grid ID (site), season,
 year, and surveillance method. The number of raccoons that were sampled
 and were positive for rabies and the number of raccoons that were sampled
 and were found to be negative for rabies are provided.SurveillanceData.csvCovariate data by grid IDThis file contains covariate data associated with the grid ID (site). The 'Site' column in this file corresponds to the "Site" column in the surveillance data file. For each site the latitude and longitude of the grid cell centroid is provided. If the grid cell was in the ORV zone (at least half of the cell was within the zone) the "ORV" column gives a 1 if the cell was in the zone and a 0 if not. The percentage of habitat cover for the grid cell is provided for the following habitat types: Cult (cultivated crops), DFMF (deciduous and mixed forest cover), EVF (evergreen forest cover), Hay (hay and pasture land), MedHi (medium and high development areas), OpenLow (open and low development areas), and WT (wetlands).CovariateData.csv