A multi‐state occupancy modelling framework for robust estimation of disease prevalence in multi‐tissue disease systems

Abstract

1. Given the public health, economic and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi-tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues. 2. We developed a multi-state occupancy modelling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained). 3. We applied this framework to pseudorabies virus (PrV) DNA detection data obtained from whole blood; and oral, nasal and genital mucosa of 510 feral swine Sus scrofa during the years 2014-2016 in Florida, USA. 4. The constrained model was better supported by data. PrV prevalence estimates varied among tissues and were higher than the naïve estimates, ranging from to 0.06 (

Three's Company: Anthropogenic factors shape co-occurrence in a meso-carnivore community in a semi-arid shared landscape of western India

“…To do this, we used the model described by MacKenzie et al (2002), which allows for estimating two key parameters: occupancy probability (ψ) and detection probability ( p ). To model detection, we constructed a model set for each species where ψ was modelled as a function of two predetermined habitat covariates and detection either (i) remained constant (null), or (ii) varied as a function of survey method (camera trap or indirect sign survey) — to account for the integration of the two types of survey data (Chaudhary et al, 2020) or (iii) varied as a function of method and associated survey effort. The covariates in the best supported model were used to model detection probability ( p ) across all steps described in the following section.…”

Improved predictions and forecasts of chronic wasting disease occurrence using multiple mechanism dynamic occupancy modeling

“…Dynamic occupancy models (DOMs) are a class of occupancy models that can estimate predictions and forecasts for species occurrence patterns, including colonization and extinction (Broms et al 2016). They have previously been applied to improve prevalence estimates in multi‐tissue disease systems (Chaudhary et al 2020) and to estimate freedom from disease (Davis et al 2019); however, to our knowledge they have never been used with mixing functions to model complex wildlife disease spread and persistence patterns. We chose DOMs because these models can incorporate mixing functions that partition disease monitoring data into distinct ecological mechanisms (MacKenzie et al 2003, Broms et al 2016), account for correlated error structures present in spatiotemporal data, and account for imperfect detection in occupancy that is inherent to disease surveillance data and that may bias parameter estimation (Mosher et al 2019).…”

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

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