Disease-structured N-mixture models: A practical guide to model disease dynamics using count data.

ABSTRACT: Obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. While multistate mark-recapture models can produce high-quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark-recapture techniques. Recently developed N-mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. N-mixture models are a type of state-space model in which observation error is attributed to failing to detect some individuals when they are present (i.e.,

SUBMITTER: DiRenzo GV 

PROVIDER: S-EPMC6362444 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature