Size matters: Sample size assessments for chronic wasting disease surveillance using an agent-based modeling framework

Aniruddha Belsare, Matthew Gompper, Barbara Keller, Jason Sumners, Lonnie Hansen, Joshua Millspaugh

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number100953
JournalMethodsX
Volume7
DOIs
StatePublished - 2020

Keywords

  • Agent-based modeling
  • Harvest-based surveillance
  • Iterative analysis using an agent-based modeling framework
  • Iterative model analysis
  • NetLogo

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