Estimating Loss of Brucella Abortus Antibodies from Age-Specific Serological Data In Elk

J A Benavides,P C Cross,B M Scurlock,E J Maichak,D Caillaud,D Caillaud,W H Edwards

doi:10.1007/s10393-017-1235-z

J A Benavides, P C Cross + Show 5 more

Open Access

https://doi.org/10.1007/s10393-017-1235-z

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Abstract

Serological data are one of the primary sources of information for disease monitoring in wildlife. However, the duration of the seropositive status of exposed individuals is almost always unknown for many free-ranging host species. Directly estimating rates of antibody loss typically requires difficult longitudinal sampling of individuals following seroconversion. Instead, we propose a Bayesian statistical approach linking age and serological data to a mechanistic epidemiological model to infer brucellosis infection, the probability of antibody loss, and recovery rates of elk (Cervus canadensis) in the Greater Yellowstone Ecosystem. We found that seroprevalence declined above the age of ten, with no evidence of disease-induced mortality. The probability of antibody loss was estimated to be 0.70 per year after a five-year period of seropositivity and the basic reproduction number for brucellosis to 2.13. Our results suggest that individuals are unlikely to become re-infected because models with this mechanism were unable to reproduce a significant decline in seroprevalence in older individuals. This study highlights the possible implications of antibody loss, which could bias our estimation of critical epidemiological parameters for wildlife disease management based on serological data.

Highlights

Many disease monitoring programs in wildlife rely on serology to infer disease prevalence (Gilbert et al 2013)
Given the vaccination campaigns to control brucellosis in elk on Wyoming feedgrounds (Maichak et al 2017), we focused on estimating how antibody loss can affect the calculation of the basic reproduction number R0
Three datasets were used in this study for different purposes: (1) The first dataset was used to reconstruct the age– seroprevalence curve, estimate the effect of age on seroprevalence and estimate antibody loss using the approximate Bayesian computation-sequential Monte Carlo (ABCSMC) model fit to age–seroprevalence data, (2) the second dataset included individuals tested over multiple years to estimate antibody loss independently of the ABC-SMC, and (3) the third dataset was used to estimate brucellosis disease-induced mortality from collared animals

Summary

Introduction

Many disease monitoring programs in wildlife rely on serology to infer disease prevalence (Gilbert et al 2013). Estimating the probability of antibody loss, referred here as the annual probability of a previously infected seropositive individual becoming seronegative, typically requires following individuals for several years post-infection (Clements et al 1982; Fredriksen et al 1999). This requires ensuring that the individual does not get re-exposed. We use a similar Bayesian modeling approach to Toni et al (2009) to fit different disease scenarios to age-specific seroprevalence data in order to estimate the annual probability of antibody loss

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