Disease‐structuredN‐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., 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.

Conservation translocations for amphibian species threatened by chytrid fungus: A review, conceptual framework, and recommendations

“…Information on Bd dynamics will help distinguish mortality associated with Bd from other sources of mortality. Where possible, postrelease monitoring should incorporate swabbing of captured individuals to determine Bd related mortality and infection dynamics, which may be estimated using capture-mark-recapture models or disease-structured Nmixture models for unmarked animals (DiRenzo, Che-Castaldo, Saunders, Grant, & Zipkin, 2019). Practitioners should be aware that swabs often fail to detect low-level infections and that multiple swabs per individual may be warranted (Shin, Bataille, Kosch, & Waldman, 2014).…”

SARS-CoV-2: Cross-scale Insights from Ecology and Evolution

“…Statistical ecologists deploy another suite of tools. Bayesian joint-likelihood models are well suited to integrating multiple datasets with different units and temporal/spatial scales and can be designed to account for mechanism (e.g., [85]) or discover statistical patterns (e.g., [86]). Species distribution models classify and predict habitat suitability for a given species on the basis of environmental factors and known species occurrence [87].…”

“…State-space models account for imperfect observations in time series data by separating the dynamics of the biological process (e.g., infection dynamics) from noise or bias in the observation process (e.g., false negatives) [91]. Extensions of these two methods can incorporate multiple infection states [93], estimate transmission and recovery rates [86], and include multiple host or virus species [94]. entities (e.g., cell, tissue, organ, person, or population) that are classified by their infection state: susceptible (S, green), infectious (I, purple), and recovered (R, blue).…”

Advances in modelling demographic processes: The Euring 2017 Analytical Meeting

“…Increasingly, the advantages of using Bayesian approaches with Monte Carlo samplers are being recognised, again facilitated by the provision of readily applicable tools or coded examples that biologists with less programming experience can readily adapt (Kéry & Schaub, ). DiRenzo, Che‐Castaldo, Saunders, Campbell Grant, & Zipkin () provide a practical guide to using n‐mixture models in disease ecology with plenty of example r code and Signer et al. () present animal movement tools (amt) an r package for managing tracking data and conducting habitat selection analyses.…”