Abstract

The relationship between host density and parasite transmission is central to the effectiveness of many disease management strategies. Few studies, however, have empirically estimated this relationship particularly in large mammals. We applied hierarchical Bayesian methods to a 19-year dataset of over 6400 brucellosis tests of adult female elk (Cervus elaphus) in northwestern Wyoming. Management captures that occurred from January to March were over two times more likely to be seropositive than hunted elk that were killed in September to December, while accounting for site and year effects. Areas with supplemental feeding grounds for elk had higher seroprevalence in 1991 than other regions, but by 2009 many areas distant from the feeding grounds were of comparable seroprevalence. The increases in brucellosis seroprevalence were correlated with elk densities at the elk management unit, or hunt area, scale (mean 2070 km2; range = [95–10237]). The data, however, could not differentiate among linear and non-linear effects of host density. Therefore, control efforts that focus on reducing elk densities at a broad spatial scale were only weakly supported. Additional research on how a few, large groups within a region may be driving disease dynamics is needed for more targeted and effective management interventions. Brucellosis appears to be expanding its range into new regions and elk populations, which is likely to further complicate the United States brucellosis eradication program. This study is an example of how the dynamics of host populations can affect their ability to serve as disease reservoirs.

Highlights

  • The relationship between host density and parasite transmission is fundamental to understanding infectious disease dynamics as well as implementing control strategies [1,2]

  • In our initial set of a priori models, models that assumed hunt areas with the same covariates were exchangeable (EX) generally performed better than spatial convolution models that accounted for correlations among neighboring areas (BYM, Table S2)

  • BYM models tended to have higher deviance information criterion (DIC) values compared to structured EX models, and the posterior distributions of yb and ya shifted downwards compared to the prior expectation (yb: 0.40 (0.04) and ya: 0.40 (0.04), Model 12) indicating that the random heterogeneity effects were more important than the spatial neighborhood effects

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Summary

Introduction

The relationship between host density and parasite transmission is fundamental to understanding infectious disease dynamics as well as implementing control strategies [1,2]. Models predict that when transmission is directly proportional to host density the parasite will be unable to persist when the host density is reduced below some threshold [1,3,4] This forms the basis for using public health practices such as social distancing (e.g., school closures) to reduce the spread of pandemics [5]. In wildlife systems, this density-transmission relationship is the justification for strategies that aim to reduce the density of susceptible individuals (e.g., culling, increasing hunter quotas, sterilization and vaccination) [6,7]. Managers may need to reduce host densities to low levels before those reductions have an impact upon disease dynamics

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