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

Graziella V. DiRenzo, Christian Che-Castaldo, Sarah P. Saunders, Evan H. Campbell Grant, Elise F. Zipkin

Research output: Contribution to journalArticle

3 Scopus citations

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., false negatives). The analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. We review the challenges of modeling disease dynamics and describe how N-mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. We also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease-structured N-mixture models, and the integration of other data types with count data to inform disease dynamics. Managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. At a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.

Original languageEnglish (US)
Pages (from-to)899-909
Number of pages11
JournalEcology and Evolution
Volume9
Issue number2
DOIs
StatePublished - Jan 2019

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Keywords

  • Bayesian
  • Dail–Madsen model
  • disease ecology
  • emerging infectious diseases
  • generalized N-mixture model
  • hierarchical models
  • host–pathogen interaction
  • mark–recapture models
  • multistate models
  • occupancy model

PubMed: MeSH publication types

  • Journal Article

Cite this

DiRenzo, G. V., Che-Castaldo, C., Saunders, S. P., Campbell Grant, E. H., & Zipkin, E. F. (2019). Disease-structured N-mixture models: A practical guide to model disease dynamics using count data. Ecology and Evolution, 9(2), 899-909. https://doi.org/10.1002/ece3.4849