Estimating patterns and drivers of infection prevalence and intensity when detection is imperfect and sampling error occurs

Keyphrases

• Infection Rate 100%
• Sampling Error 100%
• Infected Individual 16%
• Quantitative PCR 8%
• Diagnostic Test 8%
• Ecological Factors 8%
• Non-detection 8%
• Disease States 8%
• Emerging Infectious Diseases 8%
• Uncertainty Sources 8%
• Observation Error 8%
• Accuracy Improvement 8%
• Prevalence Estimation 8%
• In the Wild 8%
• Intensity-based 8%
• Hierarchical Bayesian Model 8%
• Epidemiological Study 8%
• Illinois 8%
• Nave 8%
• Detection Probability 8%
• Two-source 8%
• Individual Errors 8%
• Estimation Approaches 8%
• Abundance Estimation 8%
• Wild Population 8%
• Amphibians 8%
• Batrachochytrium Dendrobatidis 8%
• Mark-recapture 8%
• Complete Data Likelihood 8%
• Snout-vent Length 8%
• Zoospores 8%
• Monte Carlo (MC) Simulation 8%
• Observation Uncertainty 8%
• New Analytical Method 8%
• Weighted Estimates 8%

Mathematics

• Disease State 50%
• Individual Error 50%
• Repeated Sampling 50%