Abstract

Quantitative models describing environmentally-mediated disease transmission rarely focus on the independent contribution of recruitment and the environment on the force of infection driving outbreaks. In this study we attempt to investigate the interaction between external factors and host’s population dynamics in determining the outbreaks of some indirectly transmitted diseases. We first built deterministic and stochastic compartmental models based on anthrax which were parameterized using information from literature and complemented with field observations. Our force of infection function was derived modeling the number of successful transmission encounters as a pure birth process that depends on the pathogen’s dispersion effort. After accounting for individual heterogeneity in pathogen’s dispersion effort, we allowed the force of infection to vary seasonally according to external factors recreating a scenario in which disease transmission increases in response to an environmental variable. Using simulations we demonstrate that anthrax disease dynamics in mid-latitude grasslands is decoupled from hosts population dynamics. When seasonal forcing was ignored, outbreaks matched hosts reproductive events, a scenario that is not realistic in nature. Instead, when allowing the force of infection to vary seasonally, outbreaks were only present in years were environmental variables were appropriate for the outbreaks to develop. We used the stochastic formulation of the force of infection to derive R0 under scenarios with different assumptions. The derivation of R0 allowed us to conclude that during epizootic years, pathogen contribution to disease persistence is nearly independent of dispersion. In endemic years, only pathogens with high dispersion significantly prevent disease extinction. Finally, we used our model in a maximum likelihood framework to estimate the parameters that determined a significant anthrax outbreak in Montana in 2008. Our study highlights the importance of the environment in determining anthrax outbreak intensity and could be useful to predict future events that could result in significant wildlife and domestic livestock losses.

Highlights

  • Many zoonoses have environmentally-mediated indirect transmission, where pathogens can persist over time in reservoirs, and transmission occurs through ingestion (e.g. chronic wasting disease (CWD), Bacillus anthracis, Brucella spp., Vibrio cholerae, and Escherichia coli 0157:H7) [1,2,3,4,5,6,7]

  • Disease dynamics can be determined by seasonally pulsed births of the host [18, 19], changes in vector abundance or parasite virulence related to climatic variables [20, 21], or changes in host stress that increase the susceptibility to parasites [22]

  • We developed a simulation experiment to determine the effect of population dynamics and seasonal forcing in model predictions

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Summary

Introduction

Many zoonoses have environmentally-mediated indirect transmission, where pathogens can persist over time in reservoirs (e.g. on or in soil, grasses, or water), and transmission occurs through ingestion (e.g. chronic wasting disease (CWD), Bacillus anthracis, Brucella spp., Vibrio cholerae, and Escherichia coli 0157:H7) [1,2,3,4,5,6,7]. Infected hosts contribute pathogens to these environmental reservoirs via pathogen shedding or host death. Predicting the transmission dynamics in these systems is challenging, as data collection on pathogen persistence, host/ pathogen contact, and infection rates are difficult to measure empirically. Classical compartmental models focused on directly transmitted diseases in populations can be extended to environmentally-mediated indirect disease transmission [3, 4, 14,15,16]. Many directly and indirectly transmitted infectious diseases show seasonal patterns in their population dynamics triggered by different intrinsic and extrinsic factors [9, 17]. In order to effectively control and predict the outcome of disease outbreaks, it is crucial to tease apart the influence of the different factors that determine disease dynamics [23]

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