Abstract

Epidemiological surveillance for many important wildlife diseases relies on samples obtained from hunter-harvested animals. Statistical methods used to calculate sample size requirements assume that the target population is randomly sampled, and therefore the samples are representative of the population. But hunter-harvested samples may not be representative of the population due to disease distribution heterogeneities (e.g. spatial clustering of infected individuals), and harvest-related non-random processes like regulations, hunter selectivity, variable land access, and uneven hunter distribution. Consequently, sample sizes necessary for detection of disease are underestimated and disease detection probabilities are overestimated, resulting in erroneous inferences about disease presence and distribution.We have developed a modeling framework to support the design of efficient disease surveillance programs for wildlife populations. The constituent agent-based models can incorporate real-world heterogeneities associated with disease distribution, harvest, and harvest-based sampling, and can be used to determine population-specific sample sizes necessary for prompt detection of important wildlife diseases like chronic wasting disease and bovine tuberculosis. The modeling framework and its application has been described in detail by Belsare et al. [1]. Here we describe how model scenarios were developed and implemented, and how model outputs were analyzed. The main objectives of this methods paper are to provide users the opportunity to a) assess the reproducibility of the published model results, b) gain an in-depth understanding of model analysis, and c) facilitate adaptation of this modeling framework to other regions and other wildlife disease systems.•The two agent-based models, MOOvPOP and MOOvPOPsurveillance, incorporate real-world heterogeneities underpinned by host characteristics, disease spread dynamics, and sampling biases in hunter-harvested deer.•The modeling framework facilitates iterative analysis of locally relevant disease surveillance scenarios, thereby facilitating sample size calculations for prompt and reliable detection of important wildlife diseases.•Insights gained from modeling studies can be used to inform the design of effective wildlife disease surveillance strategies.

Highlights

  • Agricultural and Biological Sciences Wildlife disease surveillance, agent-based simulation modeling Iterative analysis using an agent-based modeling framework Not applicable Model code, data (GIS files, population snapshots), documentation, and model output files are all available for download here: MOOvPOP https://www.comses.net/codebases/5585/ releases/2.2.0/ MOOvPOPsurveillance https://www.comses.net/codebases/5576/releases/2.2.0/ R code files for analysis of model output data, with links to relevant model output files in a Github repository, are available here: https://github.com/anyadoc/ FranklinCWDsurveillance_Rcode

  • Sampling biases associated with harvest and spatiotemporal heterogeneities in disease distribution may result in biased estimates and erroneous inferences about disease presence and distribution

  • Chronic wasting disease (CWD) surveillance of wild cervid populations in North America is a case in point

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Summary

Introduction

We used data from five MOOvPOP iterations (output file deerpopdyFranklinCounty.csv) to assess finite population growth rate (lambda) and age structure of the model-generated deer populations. Output file for the five MOOvPOP iterations is available here: https://github.com/anyadoc/FranklinCWDsurveillance_Rcode/blob/master/ deerpopdyFranklinCounty_5iterations.csv We completed 100 MOOvPOP iterations and analyzed the 26th year population snapshots to assess the congruence of model-generated populations with field estimates for Franklin County deer population (abundance, age structure and sex ratio).

Results
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